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Survey Tools for Assessing Service Delivery

Survey Tools for Assessing Service Delivery

Jan Dehn

Ritva Reinikka

Jakob Svensson

June 2002

_____________________

Contributions and comments of Magnus Lindel?w and Aminur Rahman are gratefully acknowledged.

Contents

1. Introduction 1

2. Public spending, service delivery, and outcomes 2

3. Public sector agencies 2

4. Key Features of PETS and QSDS 5

A. Key features 5

B. Other survey-based tools 6

C. Uses and applications of PETS/QSDS 8

5. How to design and implement a PETS and QSDS 11

A. General 11

B. Fact-finding, consultations and purpose of the study 11

C. Concept Paper 12

D. Rapid data assessment13

E. Local consultant 13

F. Questionnaire design 13

G. Training and field-testing 16

H. Data entry and verification 16

I. Analysis and reporting 16

J. Ownership 16

K. Linking PETS/QSDS with other surveys 17

L. Panel data 17

6. Sampling strategy and data availability 18

A. Unit of Study 18

B. Data availability 18

C. Private for-profit and not-for-profit 19

D. Sample frame, sample size, and stratification 19

E. Sampling issues involved in linking PETS and QSDS 20

F. Non-sampling error and field-testing 20

7. Findings from PETS 21

21

A. General

B. Leakage (capture) of public funds 21

C. Incentives to staff (moral hazard) 26

8. Findings from a QSDS 28

Findings and emerging issues 30

Bibliography 33

1. Introduction

Central government budget allocations can be poor predictors of the actual quantity and quality of public services in poor countries with weak governance, a conclusion supported by the weak relationship between public spending and growth and social development indicators in cross-country analysis. There is also evidence that government funds are spent on the wrong goods (private rather than public) and the wrong people (better-off rather than the poor), fail to reach intended service providers, or are converted into actual services inefficiently. Additionally, households may choose not to take advantage of such services for various reasons.

The ability to diagnose and measure problems of service delivery within the public and private delivery systems is a pre-requisite to designing policy reforms and institutions to improve service delivery. This paper argues that micro-level tools are necessary to assess both the quality and quantity of services, and the complexities involved in transforming budgets into goods and services. Public Expenditure Tracking Surveys (PETS) and Quantitative Service Delivery Surveys (QSDS) have been developed in full recognition of the characteristics of public organizations, and with the specific objectives of identifying where service delivery problems arise, quantifying the relative importance, discovering how and why they arise, and devising means of resolving them. PETS and QSDS gather quantitative data on a sample survey basis, including inputs, outputs, and other characteristics, directly from the service-providing unit. PETS assess (often diagnostically) the issue of leakage of public funds or resources prior to reaching the intended beneficiary. QSDS take a service-facility-based approach to assessing incentives for facility level staff to produce high-quality services, and provide a measure for efficiency at the level of the frontline provider and information about determinants.

This paper is intended to guide those who wish to undertake the PETS/QSDS. The paper is organized as follows. Section 2 outlines the motivation for developing PETS and QSDS, namely the persistent failure to observe close relationships between public sector spending and outcomes. Section 3 discusses general features of public service agencies that provide public services and some reasons why information on performance is not easy to obtain. Section 4 summarizes key features and potential uses of PETS and QSDS, including a comparison with other tools for public expenditure and service delivery analysis. In section 5, we highlight key lessons learned through implementing these surveys and provide details on how to design and implement PETS and QSDS. We discuss sampling strategies and data issues in section 6. Sections 7 and 8 present findings from a number PETS studies and the first QSDS in health care in Uganda.

2. Public spending, service delivery, and outcomes

Conventionally central government budget allocations are used as indicators of the supply of public services. It has become increasingly clear, however, that budget allocations can be poor predictors of the actual quantity and quality of public services, especially in countries with poor governance and weak institutions. A substantial body of cross-country empirical studies shows that the associations between public spending on the one hand and growth and social development outcomes on the other are ambiguous at best. For example, Kormendi and Mequire (1985) and Ram (1986) find that higher government expenditures are associated with higher growth, while Landau (1986), Barro (1991), Dowrick (1992), and Alesina (1997) find higher government expenditures to be associated with lower growth. Easterly and Rebelo (1993) find that overall public investment has a very low impact on growth, and Devarajan, Swaroop, and Zou (1996) observe that the standard candidates for productive expenditures have either a negative or an insignificant relationship with growth.

Sector specific studies have likewise failed to reveal a strong link between spending and outcomes. With respect to public spending and educational outcomes, the relationship between the level of resources spent on schooling and educational outcomes remains weak (Hanushek 1995). One part of the problem seems to be that government funds do not reach schools intact, as studies in Uganda (Reinikka and Svensson 2001) and Indonesia (James, King, and Suryadi 1995) have shown

Cross-country studies of the health outcomes have come to a fair consensus on two points. First, socioeconomic characteristics explain nearly all of the variation in the mortality rates across countries. These include gross domestic product (GDP) per capita, distribution of income, and level of female education (Filmer and Prichett 1999). Second, public expenditure on health care has had little impact on average health status. According to a review by Filmer, Hammer and Pritchett (2000), the share of public spending on health care is not a significant determinant of health outcomes, such as life expectancy and child mortality. In a recent paper also using cross-country data, Rajkumar and Swaroop (2002) show that the impact of public spending on human development outcomes depends on governance measured by level of corruption and quality of bureaucracy. When governance is better so is the effect of spending on education and health outcomes.

3. Public sector agencies

Two key questions in public policy today are (1) why does the level of public expenditure on average have such a limited effect on human development outcomes? and (2) what can be done to improve performance?

The literature provides at least four interrelated explanations. First, governments may be spending on the wrong goods or the wrong people. A large portion of public spending on health and education is devoted to private goods—ones where government spending is likely to crowd out private spending (Hammer, Nabi, and Cercone 1995). Many benefit incidence studies of health and education spending also show that benefits accrue largely to the rich and middle-class, while the share going to the poorest 20 percent (where it can make a difference) is always less than 20 percent (Castro-Leal and others 1999).

Second, even when governments spend on the right goods or the right people, the money fails to reach the frontline service provider. A PETS study in Uganda in the early 1990s showed that only 13 percent of nonwage recurrent expenditures for primary education actually reached the primary school (Reinikka and Svensson 2001). Furthermore, much of the variation in funding received across schools could be explained more by political economy than by efficiency and equity considerations. Similar findings have been obtained in Ghana and Tanzania (for a review see Reinikka and Svensson 2002a).

Third, incentives to deliver a high quality product or service are often missing. Even when the money reaches the primary school or health clinic, the service providers may be poorly paid and ineffectively monitored. Clients meanwhile, have limited information to enable them to hold service providers accountable.

Fourth, even if the services are effectively provided, households may not take advantage of them. For economic and other reasons, parents pull their children out of school or fail to take them to the clinic. This “demand-side” failure often interacts with the supply-side failures to generate a low-level of public services and outcomes among the poor.

All four explanations, and in particular the second and third point, highlight a key motivation for undertaking a QSDS/PETS, namely that improving public services must involve an explicit treatment of incentives within the public sector.1 The organizational structure of public sector agencies involves multiple tiers of management and frontline workers. Multiplicity is also a key aspect of public sector agencies in terms of the tasks they perform and stakeholder or constituencies they serve. Tasks and interests at each tier may conflict with each other from the viewpoint of limited resources and finite time, and various stakeholders may also have conflicting interests. The output of public service agencies is often difficult to measure and systematic information is rarely available about specific inputs and outputs of service delivery, particularly in developing countries.

1 Dixit (2000) reviews the characteristics of public organizations in light of incentives and organization theory. For further details, see also Bernheim and Whinston (1986), Dixit (1996, 1997), Holmstr?m and Milgrom (1988), Martimort (1992), and Stole (1991).

Public sector organizations share the following key features:

? A multi-tier organizational structure, with various tiers of government down to the frontline provider;

? Multiple stakeholders with different and often competing objectives and an underlying political economy through which the stakeholders exert pressure on

public sector agencies to achieve their respective goals;

? Multiplicity of tasks with various degrees of substitutability; and

? Vague measurability of outputs and tasks.

Dixit (2000) gives an example of public education to illustrate some of these features. Multi-tasking includes providing literacy and numeracy and other direct skills, supporting the emotional and physical growth of the children, providing vocational skills and preparing pupils for working life, providing skills in health and financial management, instilling citizenship, overcoming the disadvantages of home life, and ensuring children can grow up in a drug and violence-free environment. While they are not mutually contradictory, these goals must compete for limited resources of schools and teachers and are frequently considered interchangeable in the schools’ production process. Moreover, it is very difficult to measure the output of many of these tasks. The diverse body of stakeholders includes parents and children, teachers and their unions, taxpayers, potential employers of graduates, society as a whole, private schools, and various groups favoring or opposing specific components of the curriculum. These principals have diverse preferences and objectives. Parents want “good education” and day care for their children, teachers and unions want higher pay, taxpayers want low costs, employers want vocational skills, society wants good citizens, and private schools compete for pupils and some public funds. Again, many of these objectives are mutually conflicting, for instance taxpayers’ objective of low costs vis-à-vis teachers and unions’ goal who want higher pay. And yet teachers are often not interested in pay alone but in challenging work and career prospects.

These features not only complicate analysis of performance; they may also explain why so little detailed information exists on public sector performance. First, there is often inadequate information on outputs, possibly because management information systems tend to be unreliable in the absence of adequate incentives to maintain them on a regular basis. Lack of information weakens the agents’ (that is, public service agencies and/or their employees) incentive to make the required effort—a straightforward result of moral hazard—which in turn adversely affects both the quality and quantity of services. Lack of information can be further entrenched if government allocates expenditures away from areas of poor measurability and verifiability precisely because of poor measurability and verifiability rather than on the technical and economic merits of such expenditures per se.

Second, there is often inadequate information on agent actions. Agents can often resist effective monitoring in the presence of multiple tasks and multiple principals with substitutable objectives. Moreover, as some actions are more verifiable than others, it is not always optimal to provide implicit incentives to carry out specific tasks, since the agent will then tend to divert all effort from unverifiable to verifiable tasks. In education, for example, exam results would be disproportionately emphasized. Incentive schemes are most suitable when outcomes are clearly defined and unambiguous, and become weak when neither outcomes nor actions are observable, such as in a typical government ministry.

4. Key Features of PETS and QSDS2

A. Key features

Apart from information on central government budget allocations, information on actual public spending is seldom available in many developing countries. The PETS was designed to provide such information, on a sample survey basis, from different tiers of government, including frontline service facilities with the objective of identifying the location and extent of impediments in financial flows. Government resources, which are typically earmarked for particular uses in the budget (votes, line items), flow upon release, within a pre-defined legal, regulatory, and institutional framework, passing through the layers of the government and via the banking system down to service facilities, which are charged with the responsibility of exercising the actual spending. A PETS tracks the flow of resources through these institutional strata in order to determine how much of original resources reach each level, and how long resources take to get there (if they get there at all). It is therefore useful as a device for locating and quantifying (political and bureaucratic) capture, leakage of funds, and problems in the deployment of human and financial resources. It can in principle also be used to evaluate impediments to the reverse flow of information to account for actual expenditures.

The QSDS goes beyond tracking funds to examine efficacy of spending, as well as incentives, oversight, and the relationship between agents and principals. In the QSDS, the facility or frontline service provider is typically the main unit of observation in much the same way that the firm is the unit of observation in enterprise surveys and the household is the unit in household surveys.

The QSDS can be applied to government, private for-profit and private not-for-profit service providers. In each case, data are collected both through interviews of managers and staff and from the service provider’s records (say, both local government and

2 Examples can be found at https://www.doczj.com/doc/4e18253992.html,/programs/public_services/topic/tools/.

facility). In some cases, beneficiaries are also surveyed. Triangulating the data collection this way means that information serves as a means of cross-validating information obtained separately from each source. Thus the compilation of information gathered through this method typically requires much more effort than, say, a perception survey of users, making it costlier and more time consuming to implement than its qualitative alternatives. But in many cases the benefits of quantitative analysis by far offset its cost.

As the PETS and QSDS complement each other, a PETS may be conducted in conjunction with a QSDS. Their combination allows a detailed evaluation of wider institutional and resource-flow problems on frontline service delivery. The facility-level analysis can also be linked “upstream” to the public administration and political processes (through public official surveys) and “downstream” to households through (household surveys) to combine the supply of and demand for service.

B. Other survey-based tools

A number of tools exist to analyze provider behavior, including facility modules in household surveys, benefit incidence analysis, and empirical studies to estimate facility (in particular hospital) cost functions. PETS and QSDS are distinct from these other tools in the following key respects.

First, PETS/QSDS differ from other survey-based research tools by defining the service provider (and the incentives facing the provider) as the key unit of analysis (as opposed to, say, the firm or the household). It is not unusual from household surveys to include facility modules. Previously the Living Standard Measurement Study (LSMS) household surveys included health facility modules on an ad hoc basis (Alderman and Lavy 1996). A number of the Demographic and Health Surveys (DHS) carried out in over 50 developing countries have also included a service provider component. The Family Life Surveys implemented by RAND combined health provider surveys with those of households (for a review of health facility surveys see Lindel?w and Wagstaff 2001). The rationale for including a facility module in a household survey is to characterize the link between access to and quality of public services and key household welfare indicators. These modules collect quantitative data on medical procedures, equipment and staff availability, and supervision, as well as carry out various quality checks on staff (Frankenberg and Beegle 1998; Thomas, Lavy, and Strauss 1996; Alderman and Lavy 1996).

The perspective in these surveys, however, is that of the household rather than the service provider per se. As a consequence they pay little attention to the question of why quality and access are the way they are. This is reflected in the type of data collected, which is mainly on access indicators and the range of services offered. In other words, these surveys largely ignore provider behavior and the processes and complexities, as discussed above, through which public spending is transformed into services.

In addition, in most cases, facility information is collected as a part of community questionnaires, which rely on the knowledge of one or more informed individuals (Frankenberg 2000). Information supplied by informants is therefore not only heavily dependent on the perception of a few individuals but also not detailed enough to form a basis for analysis of service delivery, such as operational efficiency, utilization, and other performance indicators. To the extent that the information is based on perceptions, there may be additional problems due to the subjective nature of the data and its sensitivity to respondents’ expectations. Perceptions are often useful as a means of formulating testable hypotheses rather than as a means of testing them.

Second, PETS/QSDS do not use budgeted costs as a basis for analysis. In benefit incidence analysis household data on consumption of public goods are combined with information on budget allocations for public expenditures to determine a unit subsidy per person. Household usage of the service is then aggregated across key social groups to impute the pattern and distribution of service provision. By contrast, PETS/QSDS collect data on actual spending and services provided at the facility level.

Third, PETS/QSDS explicitly recognize the “umbilical” link between public service providers and the rest of the public sector. Providers of public services typically rely on the wider government structure for resources, guidance about what services to provide, and how to provide them. This dependence makes them sensitive to systemwide problems in transfer of resources and the institutional framework. Therefore, it is not possible completely to isolate a public facility from the rest of the system. This sets PETS/QSDS apart from the hospital cost function literature, which despite having a clear facility focus, is more or less analogous to the firm-survey based literature. In other words, the hospital cost function literature does not try to relate the problems of service delivery to upstream issues. In part, this reflects the fact that the literature has mainly looked at cost efficiency in hospitals in the United Kingdom and the United States, where “leakage” is perhaps less of an issue than efficiency.3 Perhaps more relevant, though, is the budding literature on cost efficiency and other performance indicators in clinics and primary health facilities in developing countries (Somanathan and others 2000, McPake and others 1999). This literature can and should have an important influence on the development of the best practice QSDS.

Finally, PETS/QSDS explicitly recognize that agents in the service delivery system may have strong incentives to misreport (or not report) key data. These incentives derive from the fact that information provided by, for example a school or a health facility, partly determine its entitlement of public support/funding. Also, in case resources (including staff time) are used for other purposes (for instance in case of shirking and corruption), the agent involved in the activity will most likely not report it truthfully.

3. For a review of this literature, see Wagstaff (1989); Wagstaff and Barnum (1992); Barnum and Kutzin (1993).

Moreover, certain types of information, such as official charges, may only partly capture what is intended to be measured (e.g., the users’ costs of the service). PETS/QSDS deal with these data issues in two ways: (i) by using a multi-angular data collection strategy; that is, a combination of information from different sources; and (ii) by careful consideration of which sources and respondents have incentives to misreport, and identification of data/sources that to a lesser extent are influenced by these incentives. C. Uses and applications of PETS/QSDS

Issues of particular interest to development practitioners include efficiency, quality of service, leakage of resources, political or administrative capture, moral hazard, such as shirking and ghost workers, asymmetric information, management, supervision, and distributional issues. Many countries formulate policies within a paradigm of large and ambitious public spending programs intended to address efficiency and equity concerns. Yet, the implementation capacity of governments has seldom been systematically incorporated into the analysis of public expenditure priorities. PETS and QSDS are eminently suited for such tasks.

Figure 1. Tools of analysis of public expenditure

QSDS and PETS

Apart from diagnosis, the PETS and particularly the QSDS can be used for generating data for research. While the large literature on incentives in the context of contract and information theory focuses mostly on firms, it offers a number of applications to the public sector as well (Dixit 2000). PETS and QSDS can provide new data to test hypotheses on provider behavior underlying service delivery outcomes. Research into the economic behavior of households and firms has already yielded important insights, often with substantial policy implications. This suggests that research into public institutions, which in some countries deliver as much of half of GDP and which are directly responsible for the implementation of government policy, promises an equally rich research agenda. Important empirical research questions that the PETS and the QSDS can help to answer:

? How to design “right” incentives within the public sector (characterized by multiple principals and multiple agents) and the private sector, compatible with

increased quantity and improved quality of basic services, particularly for poor

people?

? How to measure and overcome problems of moral hazard, such as shirking and ghost workers?

? How to strengthen voice mechanisms for service users and counter problems created by asymmetric information?

? What type of accountability and oversight arrangements between various tiers of government and between government and beneficiaries can help improve basic

service delivery?

Finally, information collected through PETS and QSDS allows empirical testing of various theoretical propositions pertaining to principal-agent relations within the public sector, bargaining and other resource allocation mechanisms, as well as asymmetric information and incentive problems and how they affect the quality and quantity of services delivered. Empirical research using PETS and QSDS data can be used not only to test various predictions from standard contract and incentive theories, but also to contribute to the development of new theories in this area for public sector agencies.

Looking beyond country-specific studies, the compilation of systematic multi-country service delivery databases will also facilitate cross-country comparisons. The World Bank’s Development Research Group is presently of setting up such a database of QSDS surveys. As more data become available in the coming years, it is expected that empirical research in this area will grow.

5. How to design and implement a PETS and QSDS

A. General

PETS and QSDS are now being applied by the operational staff within the World Bank and other donors, researchers, and developing country governments. The intuitive appeal of PETS and QSDS, however, can belie the complexity involved in planning and implementing these surveys.

First, the survey teams and stakeholders need to have a clear idea of why they are doing the study. When a new analytical tool is developed, there is a temptation to apply the tool, regardless of whether the situation on the ground warrants its application, and, indeed, without tailoring the instrument to the local conditions.

Second, the planning, design, implementation, and subsequent analysis of PETS/QSDS data are complex undertakings. A successful study requires a continuous and careful hands-on effort in accordance with clearly defined objectives. In particular, survey managers should not underestimate the importance and the difficulties involved in obtaining quantitative data from facility records. Unlike household surveys, PETS/QSDS cannot be based on recall data but require consultation of records.

Finally, there is a need to consult widely with the client government and other stakeholders about the objectives of the study. Lack of ownership may result in rejection of the findings. To ensure that the study meets its ultimate objective of informing policy reforms, it is essential that consensus building among stakeholders is nurtured prior to, during, and subsequent to execution.

In light of the lessons from past surveys, we outline the steps involved in successful design and implementation of PETS and QSDS. Many steps are common for both surveys, given that PETS typically includes a facility component and QSDS needs to relate public facilities to the government system. Typical steps involved in designing these surveys include the following:

B. Fact-finding, consultations and purpose of the study

In the initial phase, the survey team needs to consult extensively with a wide array of in-country stakeholders, including government agencies (ministry of finance, sector ministries, and local governments), donors, and civil society organizations. Such consultations are likely to reveal relevant hypotheses about major problems in service delivery and hence define the purpose and objectives of the study, as well as the sector(s) to be included in the study. Given that the survey requires considerable effort, it is advisable to limit the number of sectors to just one or two. Until now, the PETS and QSDS have mostly been carried out in the “transaction-intensive” health and education

sectors with clearly defined frontline service delivery points (clinics and schools), but there is no reason to limit the use of these tools.

After identifying the sector(s), the next step is to identify key issues and problems involved in service delivery in the chosen sector. Again, broad-based consultations are useful to

? reach agreement on the purpose and objectives of the study;

? choose the sector(s) for the study;

? identify key service delivery issues and problems (research questions) in the chosen sector;

? determine the structure of government’s resource flow, rule for resource allocation to frontline facilities, and the accountability system;

? obtain a good understanding of the institutional setting of government, private for-profit and private not-for-profit providers;

? check data availability at various tiers of government and at the facility level;

? assess available local capacity to carry out the survey and to engage in data analysis and research; and

? choose the appropriate survey tool.

For instance, if the perceived problem is one of funds being captured en route to the service facility, or that facilities are not performing, given their resource receipts, or indeed a combination of both, the study can take the form of a PETS, a QSDS, or a combination of the two. In many cases a multi-purpose design is appropriate, because the performance of the facility is directly affected by the discretion, incentives, and behavior of officials at various tiers of government.

Consider the following example. Suppose the government initiates a policy change, which involves a large increase in education spending, possibly combined with decentralization and other institutional reforms. Suppose also that there are doubts about the capacity of local governments to handle the increased spending. Government may therefore decide to monitor the effectiveness of the policy change by using a longitudinal survey, combining both PETS and QSDS. A PETS would assess the actual resource flows from the central government via local governments to the frontline facility, while a QSDS would examine the efficiency with which the funds are transformed into services, and assess the supply of services.

C. Concept Paper

Once the consensus among stakeholders has been reached on the sector, issues, and survey tool, the survey team needs to prepare a concept paper. The concept paper

typically contains sectoral information on key issues to be investigated and how to investigate them. The concept paper records the stakeholders’ agreements on the modalities of the study.4

D. Rapid data assessment

A rapid data assessment may be required to determine the availability of records at various layers of the government (as well as the private sector), particularly at the facility level. In the past, some studies have failed due to unanticipated lack of records in local governments and facilities; for example, the first health facility survey in Uganda in 1996 (see section 7 for details). Be sure to verify the availability of records early on, even if it means a delay and some extra up-front costs. The fact-finding and consultations at the concept design stage often take place in the capital city so the facilities in its vicinity can be easily visited to check on records, with the proviso that they may not be representative of facilities in remote locations. Therefore it is crucial that the data assessment is not restricted to one location. For a rapid data assessment, it is advisable to design a simple questionnaire.

E. Local consultant

It is likely to be more cost-effective as well as beneficial for local capacity building to use local consultants to conduct the PETS/QSDS. In-country consultants are likely to have a comparative advantage over their international counterparts regarding knowledge of local conditions. The consultant should be selected in close consultation with the survey team to ensure adequate survey skills (an issue in many low-income countries). The technical nature of the survey work implies that enumerators need to have sufficient research and statistical training. Where such institutions have insufficient capacity, the team managing the survey must supervise the training of the enumerator closely.

F. Questionnaire design

The instrument design should ensure that recorded data collected at one level in the system can be cross-checked against the same information from other sources.

PETS/QSDS typically consist of questionnaires for interviewing facility managers as well as separate data sheets to collect quantitative data from facility records, and from local, regional, and national governmental institutions in recognition of the importance of the rest of the public sector for facility performance. The combination of questionnaires and datasheets is usually flexible enough to evaluate most of the problems under study. A beneficiary survey can also be added.

4 Examples can be found on the web https://www.doczj.com/doc/4e18253992.html,/programs/public_services/topic/tools/

A crucial component of PETS/QSDS is the explicit recognition that respondents may have strong incentives to misreport (or not report at all) certain information. To this end, it is important to carefully consider which sources and respondents have incentives

to misreport, and what data/sources that to a lesser extent are influenced by these incentives. The data collection strategy should be devised accordingly. As a general guideline, information should be collected as close as possible to the original source. This implies that data is typically collected from records kept by facility for its own needs (for example, patient numbers can be recorded using daily patient records kept by the medical staff for medical use, drug use can be derived by studying stock cards, and funding to schools can be recorded from check receipts). It is also important to keep in mind that some information (for instance on corruption) is almost impossible to collect directly (especially from those benefiting from it). Instead, different sources of information have

to be combined with a model of the agents’ behavior to indirectly derive the information that is wanted.5

To be comparable a core set of questions must remain unchanged across waves of surveys, across sectors, and across countries. Six core elements for all facility questionnaires have been identified:

? Describe the facility. Record the size, ownership, years of operation, hours of operation, catchment population, competition from other service providers, access to infrastructure, utilities and other services, and information on the range of

services provided. Information about income levels and other features of the

population living in the vicinity of the facility may also be useful (as controls for

subsequent econometric analysis).

? Inputs. Because service providers typically have a large number of inputs it may not be feasible to collect data on all of them. Some inputs are typically more

important than others. For example, labor and drugs account for 80–90 percent of

costs in a typical primary health care facility. For schools, the relevant costs are

teachers’ salaries and textbooks. In addition, there may be important capital

investments. The key point in the measurement of inputs is that they be valued in

monetary terms. This in turn requires that units be recorded carefully and

consistently and price information (for example, wages and allowances for labor)

be assembled for each key input.

? Outputs. Examples of measurable outputs include numbers of in- and out-patients treated (health care), and enrollment rates, numbers of pupils completing final

5 Or one can observe the provider over a long period of time on the assumption that agent behavior

will revert to normal due to economic necessity. Unfortunately, this is an expensive and demanding approach, which therefore limits its use. The study by McPake and others (1999),which adopted this approach, was only able to sample 20 health care facilities.

exams (schools). Outputs may be multi-dimensional. In schools, for example, the objective is often not simply to teach children to read and write, but also to provide them with vocational skills, citizenship training, and the means for overcoming

disadvantages from poor homes. Unlike inputs, outputs rarely convert to monetary values (public services are often free or considerably subsidized). Efficiency

studies frequently use hybrid input-output measures such as cost per patient.

? Quality. Quality is multidimensional, and an effort should be made to capture this multidimensonality by collecting information on different aspects of quality.

Examples of this include observed practice, staff behavior and composition, and

availability of crucial inputs. Quality could also be captured by collecting

information from the users.6

? Financing. Service provision may be sensitive to the source and continuity of financing. For example, a service provider, whose financing is intermittent will be less able to deliver services consistently than an otherwise identical provider with more reliable financing, ceteris paribus. Dependence on a single source of finance may also be risky and could weaken the bargaining position of the facility relative to facilities with multiple sources of finance. Information should therefore be

collected on sources of finance (government, donor, user charges), amounts, and

type (in-kind versus financial support).

? Institutional mechanisms and accountability. Public facilities do not face the same competitive pressures as private facilities. Instead, they are subject to supervision and monitoring by central, regional, or local government institutions, civil society, political leaders, and the press. In other words, they are often multiple-principal

organizations. In addition, principal-agent relations between management and

employees that affect performance. To understand the constraints under which

public facilities operate, the information collected should recognize the multiple-

principal nature of the provider. In practice, this means collecting information on supervision visits, management structures, reporting and record-keeping practices, or parent or patient involvement, and audits. Since graft may be particularly

6. Donabedian (1980) distinguishes between structural, process, and outcome quality. Structural quality relates to physical inputs and personnel as well as organizational arrangements. Process quality pertains to the functions carried out at the facility, where the emphasis is on the extent of staff compliance with “best practice” procedures. Finally, quality of outcome refers to standard measures of outcomes, such as pain and suffering and patient satisfaction in the case of health care. The problem of the “trilogy” approach lies in the tenuous links between different dimensions of quality. For example, quality of structural inputs by no means assures good care. Similarly, the link between process and outcome is not clear; it is likely to vary by process, and may not be visible for a long time. Favorable outcomes are often affected by factors not under direct control of the health worker. This then raises the question of how to weight different dimensions of health care quality.

relevant in the case of public sector performance, the survey instrument design

needs to capture the sensitive nature of issues such as leakage of resources or

moral hazard (shirking, ghost workers).

Variations to this basic template might include the addition of different modules to test specific hypotheses as has been done extensively in LSMS household surveys.

G. Training, field-testing, and implementation

Once the survey instruments (questionnaires and data sheets) are drafted according to the specific needs of the study, the next step is to train the enumerators and their supervisors. After the completion of the training, survey instruments should be field-tested and then finalized for implementing the actual survey. Supervision of enumerators is critical during implementation. It is also good practice to prepare a detailed implementation manual for the survey personnel.

H. Data entry and verification

Cost frequently limits the survey teams’ ability to monitor the data collection process continuously. It is more cost effective to do spot checks and make field visits during data collection to discover problems of implementation in time to make the necessary adjustments. An alternative is to undertake an ex post check on data entry. In either case, the team will need to scrutinize the completed questionnaires and the data files, and, where necessary, request return visits to facilities and/or to various levels of the government. The output from this stage is the complete data set.

I. Analysis and reporting

Depending on the capacity of the survey consultant, the analysis is typically done either by the survey management team or by the consultant in conjunction with the team. The final report and analysis should be widely disseminated to encourage debate and discussion to facilitate the alleviation of the problems highlighted in the survey and for necessary policy reforms to ensure better service delivery. The success of reforms and problem alleviation, however, to a great extent depends on the ownership issue, as discussed below.

J. Ownership

Ownership is crucial not only for successful implementation of the survey itself but also for its impact. Public sector reform is often a difficult political issue requiring a process that involves both government and civil society. A necessary first step is therefore to ensure that the ownership of the PETS/QSDS rest within government and/or civil society of the country.

One way to overcome the ownership-capacity tradeoff is to implement the survey as a collaborative effort between government, local research institutions and/or statistical office, and donors. A key feature such collaboration is that local institutions become heavily involved in the survey design, its implementation, and analyzing the results. Experience shows that this can be a highly successful strategy. In Uganda, for example, while the first PETS in 1996 was initiated by the World Bank, the subsequent surveys in education and health were undertaken at the initiative of government and implemented by a local consultant (Government of Uganda 2000, 2001, 2002).

K. Linking PETS/QSDS with other surveys

Linking the PETS/QSDS with the household surveys (to include the demand for services) or public official surveys (to include political economy and administration issues), would obviously allow a much more comprehensive analysis of service delivery, and will be implemented in the future. The supply of services must ultimately be valued in terms of the outcomes, that is, the benefits they confer upon their intended beneficiaries. For households this means household welfare, while for firms, which consume publicly provided services such as security and those provided by utilities, a relevant outcome variable may be profits or dividends. The effect on outcomes is not just a function of the smoothness of resource flows through the government hierarchy and their conversion to services at facility level; a final critical link is that between the service provided and household and firm outcomes. That is future work it is desirable to link the PETS/QSDS with a household survey (or a firm survey) in order to determine impact of services on welfare and profits as well which aspects of quality matter to consumers and which services are in demand.

L. Panel data

For a careful policy evaluation, it may be useful to design the survey instruments in such a way that the data have a panel dimension. Unless the policy change affects a subset of facilities, it is generally not possible to evaluate its effectiveness using only cross-section data, because it does not uniquely distinguish the effects of the policy changes from those of other contemporary universal shocks.

The time dimension of the rounds of surveys depends on the speed with which policy changes translate into outcomes, that is, the time it takes for the policy change to be reflected in actual changes in spending, the speed with which the spending changes affect actual service delivery, and the time it takes the change in service delivery to produce changes in outcomes. Several years of data may be needed, either by returning to the facility each year, or, in the case of ex post policy evaluations, by collecting data on several time periods at once during the same visit. Thus, five years of data was collected from schools in Uganda, while the Tanzanian school survey collected three years of data (Ablo and Reinikka 1998, and Government of Tanzania 1999).

6. Sampling strategy and data availability

In this section we discuss sampling strategy, including the choice of the unit of study, ownership categories, sample size, and non-sampling error.

A. Unit of Study

Most sectors deliver services to the public from different service delivery points. In the education sector, for example, state-funded services are provided from primary schools, secondary schools, universities, various other types of technical colleges, and even from work, social, and community places, such as hospitals, churches, mosques, and community group meetings. Similarly, the legal system delivers justice (or order) via courts, prisons, and police stations. For a QSDS, hence an important question is what should be the units of study?

The unit of study is based on three considerations. Is it an important vehicle for delivery of the service in question? Is the unit of study sufficiently numerous to allow statistical analysis? Are the units relatively homogenous?

A reasonably homogeneous sample becomes important for analyzing cost efficiency, as unit cost substantially varies across different types of facilities (for instance, consider the case of a large hospital vis-à-vis an aide post). Of these three criteria, it is almost the most difficult one to ensure. Official classification systems, say for health facilities, may be completely out of date, or alternatively more indicative of the desired future status of facilities than their actual current characteristics. For example, in Mozambique and Uganda where new health facility are being sorted into a new classification system, a modest size facility can be listed as a major regional health centers on the grounds that the intention is to gradually upgrade it to that standard. Meanwhile other facilities in the same register correspond reasonably well with the new classification ranking system. In such circumstances—where official classification systems may thus be a poor guide for homogenous sampling—it may be necessary to supplement the official classification systems with information collected during the actual survey on the functional aspects of service delivery to enable a second-round ex post reclassification.

B. Data availability

The risks relating to data availability arise from two sources. First, there may be notable differences in financial and management arrangements across regions within a country. In particular, the relationships between facilities and other segments of the public sector in budget execution may not be uniform. Second, although records may appear to be kept in good order for some providers, this is not necessarily the case for others whose control systems may be weaker and/or the necessary data may be unavailable. Data risks should not be underestimated. Survey instruments have to be

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