Indicators for Urban and Regional Planning
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Indicators for Urban and Regional Planning

The Interplay of Policy and Methods

Cecilia Wong

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eBook - ePub

Indicators for Urban and Regional Planning

The Interplay of Policy and Methods

Cecilia Wong

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About This Book

This book focuses on the measurement and utilisation of quantitative indicators in the urban and regional planning fields. There has been a resurgence of academic and policy interest in using indicators to inform planning, partly in response to the current government's information intensive approach to decision-making. The content of the book falls into three broad sections: indicators usage and policy-making; methodological and conception issues; and case studies of policy indicators.

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Publisher
Routledge
Year
2006
ISBN
9781134495917

PART I

INDICATOR USAGE AND POLICY-MAKING

CHAPTER 2

INDICATORS AND POLICY-MAKING

Following on from the epistemological discussion of the nature of indicators in Chapter 1, this chapter further examines the relationship between theory, measurement and public policy-making. It then identifies the dilemma and tension faced by social scientists, and the role they can play in indicator research. It concludes by outlining some longstanding institutional and managerial issues involved in the use of indicators in policy-making.

THEORY, MEASUREMENT AND POLICY-MAKING: THE TANGLED TRIANGLE

The debate over the nature and purpose of indicator research has largely been focused on two dichotomies: theoretical versus empirical, and basic scientific versus valuative. The relationship between theory and measurement has long been a subject of debate. In the mid-nineteenth century, Auguste Comte (1844: 25) condemned observation without theory as ‘empiricism’ and theory without observation as ‘mysticism’. The contention between empirical measurement and theoretical ideas is strongly manifested in indicator research. The empiricist holds the view that data collection comes first and working out its meaning comes later, while the theorist insists on having some sort of a priori theoretical model to guide the selection and interpretation of data. As a matter of good practice, the advice from most social research text is that measurement should be guided by theories (e.g. Babbie 1992; Bulmer 1977) to avoid amassing data without giving precise definitions to guide policy action (De Neufville 1975; Fox 1974). After examining a series of James Coleman's reports on education, MacRae (1985) criticised them as empirical studies set off from common sense, which could easily lead to bias and be manipulated by decision-makers.
While the theory–data nexus is widely accepted as the norm, there is also a realisation that the view of a one-way, linear relationship between theory and data has been oversimplified. Ragin (1987) argued that a divide between theories and concepts, on the one hand, and data gathering and analysis, on the other, tended to undermine the potential of the data collected. For pragmatic purposes, it is inevitable to find that theory and measurement mesh in an iterative loop during the process of indicator development. As discussed later in Chapter 5, one of the major millstones of indicator research is the lack of robust and reliable data. Hence, Ragin's assertion that initial examination of data usually exposes the inadequacy of theoretical formulations, and further data analysis can lead to progressively more refined concepts, offers a more realistic description of the actual practice of indicator research.
Turning to the other dimension of the argument, the focus is on whether indicators should be policy-related or scientific measurements of social change (MacRae 1985). As discussed in Chapter 1, the value-laden policy dimension of indicators clashes with both the rationalist and the empiricist ideologies. The mix of objective measures and normative policy action makes indicators an enigma in social research. In order to defend the scientific purity of indicators, many social scientists make it explicit that indicators should be used to advance the state of social theory and have to be explored from a theoretical basis and in the context of causal social models (Sheldon and Moore 1968; Land and Felson 1976; Land and McMillen 1980). However, if statistics and indicators are used to serve public debate and policy action, then they have to be more than pure research tools of analysis (MacRae 1985; Miles 1985).
The discussion so far suggests that the dichotomy of theory and empirical measurement should not be overstated and they should be treated as two sides of the same coin in indicator research. Opinions regarding the purpose of indicators as tools for policy or scientific domains are somewhat diverse. The demarcation between the two sets of concern again may be more of an intrinsic academic debate rather than what matters in reality. The phenomenon and concept to be measured in many cases is not a static object but a moving beast. Scott Greer's (1969) discussion of the changing nature of problem definitions over a period of time best explains such a dynamic process in policy circles. He argued that a public problem couching on folk frame of reference will soon develop to a policy problem through seeking solutions, and after systematic inquiry it will in turn become a scientific problem. If policy context and experience is, as proposed by Innes (1990), treated as a form of knowledge, then the boundary between the two will be blurred. In recent years, many indicator projects set out to measure concepts such as ‘sustainable development’, ‘economic competitiveness’ and ‘public service delivery’, which are the outcome of evolving policy discourses. One recent example is the publication of the Sustainable Communities document by the Office of the Deputy Prime Minister (ODPM 2003a) to tackle the deepening housing crisis across different parts of England. While the badge of ‘sustainable community’ is sweeping the policy community to advocate neighbourhood and housing renewal, there is still little understanding of what it exactly entails. The Urban Policy Network of the ODPM, therefore, commissioned a detailed analytical report (Kearns and Turok 2003) to unpack the key elements and characteristics of a sustainable community in order to brief its civil servants and other policymakers. The findings of the report subsequently found their way into the Egan Review (Egan 2004) of skills required to push the agenda of sustainable communities. It is also clear that policy discourses such as ‘polycentricity’, ‘spatial planning’ and ‘social exclusion’ developed from the European Union tend to find their way very quickly into British policy documents, with research and clarification coming later. These examples lend strong support to Greer's formulation and that policy context and experience should be accorded some weight on a par with theoretical knowledge to guide the design and measurement of indicators.
Notwithstanding the reality that theory, measurement and policy is closely intertwined with one another and often found as a tangled web of relationships in the process of indicator development, the axes of theory–data and basic research–policy application do offer a way to examine the nature and emphasis of different types of indicator research. Based on these two dimensions of debate, a four-fold classification of indicator research could be developed (see Table 2.1). Type I and Type II of the classification are linked to the apolitical path of basic research and fit well with the pure epistemological positions. The remaining two groups have a strong policy focus, with Type III grounded in the importance of theoretical frameworks and Type IV driven by empirical data. It is fair to say that many indicator sets developed for policy monitoring are closely following the Type IV protocol. One well-known example is the Department of the Environment's (DoE 1983) 1981 Z-score deprivation index. Other examples of this approach include consultancy research on local economic development and economic competitiveness (e.g. WMEB 1993; Pieda 1995). However, it is the valuative-theoretical approach (Type III) that has been widely advocated by researchers (e.g. Coombes and Wong 1994; De Neufville 1975; MacRae 1985) to guide indicator development.

INDICATORS AND POLICY PRACTICE: UNDERSTANDING, INTERESTS AND VALUES

The understanding of both theoretical ideas and policy contexts is of prime importance in the process of indicator development if indicators are used to inform policy decisions. The discussion here is based on an Economic and
Table 2.1 A four-fold classification of approaches to indicator measurement
Theoretical Empirical
Basic, scientific I II
Valuative, policy-oriented III IV
Social Research Council (ESRC)-funded study conducted by the author between 1995 and 1998. This research aimed to provide an in-depth study to tackle the problems encountered in developing indicators to inform local economic development (LED) decisions. The overall research design impinged on the integration of three key research components: policy, theories and methods. The project started off with a major literature review to derive an extensive list of key factors that are considered to be important to LED (Wong 1998a). Primary data was collected both through postal questionnaires and indepth interviews, so as to examine practitioners’ views on LED and indicator development and usage in two case study areas, the North West and the Eastern Region (see Wong 1998a, 1998b, 2000). These two regions were chosen because of their contrasting socio-economic conditions and experiences that would provide the conditions for a more robust interpretation of the findings. An extensive data collection exercise was carried out to compile a full dataset containing sixty-one LED indicators as well as the associated explanatory documentation. However, after an initial exploration and sensitivity testing of their statistical properties, only twenty-nine of these indicators were included in the final analysis. Multivariate analyses were then carried out to examine the structure of relationships among the compiled LED indicators (Wong 2001, 2002a) and the relative strengths of relationship between the LED indicators and various performance variables (Wong 2002a).
Despite the fact that there was not a specific policy client for the ESRC project, a significant data collection exercise was carried out to elicit views from policy-makers on LED issues, both through questionnaire surveys and in-depth interviews. The reason for doing so was two-fold: first, to corroborate and validate the theoretical findings from literature with empirical evidence from practitioners, and, second, so as to adopt the value relevance approach of Max Weber (1964), namely, to penetrate the subjective meanings that actors attach to their own behaviour and to the behaviour of others to improve the explanatory understanding of issues surrounding LED. After a review of literature, eleven factors considered to be important to LED were identified. The survey response of practitioners (see Table 2.2) confirmed these as comprising an exhaustive list. While there is a high level of stability and consistency over the perceived importance of different factors, this conclusion also highlights the fact that their relative importance is circumstantial. Even when a factor emerged as common across the two regions, the reasons behind the assigned importance can be quite different.
The empirical evidence subsequently collected from in-depth interviews debunked the logistics and myth behind the fantasy of high-tech development and the quality of life syndrome (Wong 1998a). For instance, the type of employment provided by high-tech development did not necessarily match the skills of the local residents, who tended to be semi- or unskilled labour in inner-city areas; hence, this factor was not highly regarded in the North West. The attitude towards research and development in the Eastern Region was positive but also cautious. The high-tech image projected from the Cambridge area did not seem to stimulate massive enthusiasm as, for instance, the success of the Cambridge Science Park was not seen as a realistic role model for others to follow. Equally interesting were the different views over quality of life and LED. Instead of being a contributor to the return of business investment, quality of life was seen as the consequence of prosperity. Furthermore, it was widely agreed that there were always some decent pockets of residential areas within commutable distance wherever one worked in Britain. The obvious inter-regional dimension over the ranking of quality of life in the questionnaire data was subtly shown in the interviews, in that those in the Eastern Region were more aware of the good living quality they had. Their counterparts in the North West were, nevertheless, conscious of the fact that there were many affluent suburbs and scenic Cheshire villages from where residents could commute to work elsewhere in the region.
The findings from literature and policy-makers were further tested by the assembled indicator database. Statistical findings from principal component
Table 2.2 Mean rank of local economic development factors
image
Source: Wong 1998a: 711
Note: The mean rank values of LED factors were calculated according to their importance given by the respondents in the survey; low value of mean rank implies greater importance of the factor. The value in the brackets is the number of respondents who believed that a factor was not at all important.
analysis (Wong 2002a) echo the views expressed by the majority of practitioners (Wong 1998a) that traditional factors such as location, infrastructure, finance and human resources are more important in the process of LED. The statistical analysis also lends more support to the views of academics such as Doeringer et al. (1987) on the importance of having a favourable industrial structure than to the views of practitioners in the two English regions. It is also interesting to note that the human resource factor tends to be frequently associated with the intangible factors of quality of life and business culture. This result again mirrors the findings of the in-depth interviews with practitioners (Wong 1998a) that skills and qualifications of human resources are widely perceived as important factors in successful LED.
The experience gained from this study shows that the engagement of policy-makers in the process of indicators development can serve two main purposes. The first is to enhance an understanding of the policy operation environment, the subjective values and interests that policy-makers have over the research; the other purpose is that of providing for the ultimate decision over policy choices. With regard to the former, a better understanding of the policy context and values will enhance the focus of research and provide clarity to the concept to be measured. More importantly, it will provide a frame of reference when policy recommendations are made after reporting the research findings. This viewpoint is shared by Martin that in order to influence the influentials, it is important to incorporate policy-makers’ views and needs by ‘determin[ing] the considerations and standards of evidence that they [policy-makers] will accept or require. For the most part, these concerns will not conflict with the research requirements for validity and reliability’ (Martin 1989: 48).
With regard to the issue of ultimate decision-making, it is clear that policy choices and decisions are value judgements, which cannot be scientifically determined by statistics. More importantly, it will be naĂŻve to assume that statistics and research analysis bear any direct relationship to policy decisions. The account given by Shirley Williams, a politician with a close association to academia, on the relationship between policy and research sheds some light on this debate:
Obviously a policy maker who is elected will have his or her own strong views. Those views will be shaped by the commitments already entered into the manifestos of his or her party; they will be influenced to a great extent by personal principles and prejudices; and the views will be modified by the policy maker's awareness of pressure from colleagues and what colleagues are likely to accept or reject. If the policy maker is a minister in a department, then his relations with other ministers and other departments within the government structure will influence his decisions. So will the estimates he makes of the interest groups whose help he needs to carry new policies out: will they cooperate or will they resist? Then Parliamentary opinion and public opinion will have to be taken into account. Finally, the policy maker will have to assess the need for policies, their possible effects and their costs. All this relates to any policy decision: yet of that long list only two, namely the assessment of the need for, and probable effects of, the policy and of its cost, are clearly related to research.
(Williams 1980: 2)
A fine line has thus to be drawn between role of the research analyst and those who are responsible for making decisions should they be politicians, government officials or local communities. This issue inevitably links to the debate about the role of researchers in the process of indicator development and the concern over the infiltration of policy values and interests into research findings.

THE ROLE OF SOCIAL SCIENTISTS IN INDICATOR RESEARCH

Indicators as a set of statistics do not convey any meaningful message until we make sense out of them. The analysis and interpretation process thus becomes an integral part of indicator development. In the social science community, which largely follo...

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