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Article

Improving Appraisal Methodology for Land Use Transport Measures to Reduce Risk of Social Exclusion

by
John Stanley
1,* and
Janet Stanley
2
1
Institute of Transport and Logistics Studies, The University of Sydney Business School, Sydney, NSW 2006, Australia
2
School of Design, The University of Melbourne, Melbourne, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(15), 11902; https://doi.org/10.3390/su151511902
Submission received: 3 July 2023 / Revised: 28 July 2023 / Accepted: 1 August 2023 / Published: 2 August 2023
(This article belongs to the Section Sustainable Transportation)
Review Reports Versions Notes

Abstract

:
Enabling people to be socially included is a high-priority goal for many governments but monetised benefit measures applicable to initiatives intended to reduce social exclusion risk are lacking in land use transport, and other, policy arenas. In settings where the decision-making process seeks guidance from cost-benefit analysis, this is likely to disadvantage initiatives intended to reduce exclusion. This is a particular problem for public transport services intended to enable people to access more of the opportunities available in their society (‘social transit’). This paper develops a monetised measure of the value of improved mobility as it contributes to reducing risk of social exclusion, showing this to make a material difference to benefit estimates from social transit service improvements. It also develops monetised benefit estimates for some other potential pathways for reducing risk of social exclusion, particularly changes in bridging and bonding social capital, sense of community, subjective wellbeing and neighbourhood disadvantage. The research thus provides an opportunity to significantly strengthen appraisal tools linked to reducing social exclusion, which should encourage more integrated approaches to reducing exclusion and improve implementation prospects for initiatives with that purpose. Reduced social exclusion is a likely outcome.

1. Introduction

Policy formation and program/project prioritisation in many jurisdictions is informed by cost-benefit analysis (CBA) of alternative options that have been identified to meet target needs, often framed in terms of enhancing achievement of one or more of the triple bottom-line—environmental, social, economic—sustainability goals [1]. Government appraisal guidelines frequently shape and assist the relevant processes of need identification and, more frequently, of option appraisal [2,3], as part of procedural pathways through which measures are expected to progress to demonstrate value for money.
Being able to express expected benefits and costs of different measures in money terms is an advantage in those jurisdictions that look to CBA-based appraisal for guidance on decision taking. This seems to add an element of assurance, even though appraisal guidelines commonly emphasise the importance of giving due consideration to non-monetised impacts. Multi-criteria analysis (MCA) can be used to aggregate monetised and non-monetised impacts but Hickman and Dean [4] (p. 692) suggest that “Where impacts cannot be monetised or quantified, they are often given less weight in the MCA—the quantitative evidence is given precedence”. Multi-criteria approaches also risk excluding the opinions of affected parties about measures under consideration, contrary to the fundamental individual preferences value judgement on which CBA is based, falling back instead on easier-to-obtain views of ‘experts’ or of the appraisal team [5].
In urban land use transport planning (LUTP), the social goal area of the sustainability triple bottom line is least developed in terms of benefit/cost monetisation, with appraisal guidance relatively weak on social matters [4,6,7,8]. This poses a challenge in terms of demonstrating the potential value from measures whose primary purpose is to contribute to social goal achievement, including the widely adopted goal of reducing social exclusion. This point is well recognised in the recent Socially inclusive transport strategy for England’s north:
“The difference to current ways of assessing transport schemes: Social inclusion and equality considerations are often secondary in transport decision-making when compared to factors such as journey time savings and economic benefits. The emphasis on these factors is entrenched in practice and policy and results in fundamentally different decisions to what would be the case if equivalent emphasis were placed on social inclusion.”
(Transport for the North [9] (p. 27).)
In LUTP, reducing inequality or (closely related) reducing social exclusion is an increasingly important policy goal for many governments, with London and Metro Vancouver (for example) placing it centre stage in their most recent land use transport strategies [10,11], United Nations Sustainable Development Goals 10 (Reduced Inequalities) and 11 (Sustainable Cities and Communities) also being relevant in this regard [12]. The lack of monetised benefits associated with such policy intentions, however, poses a challenge for initiatives with this purpose in jurisdictional settings that look to CBA-type analyses to help guide decisions. The evolution of broadly based approaches to government policy development and budgeting, such as New Zealand’s Living Standards Framework [13], is promising in terms of recognising multiple goals in direction-setting and associated policy formation, beyond matters that are readily measurable in money terms. However, such approaches are still at an early stage of development, with emerging performance indicators probably more suited to debates about goal-setting/need identification than to initiative prioritisation.
This issue of benefit monetisation is of particular concern for public transport services that might be described as social transit. Public transport services perform two main roles [14]. One role is mass transit, where the main societal benefits are congestion cost savings, reduced air pollution and greenhouse gas emissions, noise mitigation and agglomeration (productivity) benefits. These impacts, and user benefits, are generally measurable in monetary terms, which means that mass transit improvement opportunities are usually well-placed to be argued within CBA frameworks. The second public transport role is a social transit role, where the service again provides user benefits but with less likelihood of the societal benefits expected from mass transit. Instead, social transit services are primarily focused on providing what might be seen as mobility safety-net benefits, enabling those who cannot, or choose not to, travel by car or active transport to better meet their needs and participate in opportunities available in their society. The associated societal benefits in this case are essentially about reduced social exclusion [15].
The social transit role can be seen as having characteristics of a merit good [16]: first, if provision of this good is left solely to the private market-place, some people will consume or use less of it than is in their best long term interest, perhaps because they cannot afford more or it may not be available to them; and, second, this lower level of consumption/use by a significant number of people is recognized as leading to lower levels of both personal and wider societal well-being, the latter being an external cost [17,18]. Both these characteristics reflect market failure. In this regard, there is a growing literature on minimum or threshold public transport service levels as one way of supporting social safety-net service standards for mobility [19,20,21].
A major challenge in appraisal of the social transit role of public transport has been the lack of an accepted means of monetising the merit good-related benefit stream, which is essentially about the value of reducing risk of social exclusion. As a result, relative to mass transit initiatives and road improvements, where benefit/cost monetisation is also well advanced, there is effectively an (unintended) appraisal ‘bias’ within the land transport sector against improvements to social transit services, attributable to the comparative weakness of appraisal processes related to social outcomes.
The aim of this paper is to help reduce this appraisal bias and, in so doing, to strengthen the case for policy directions and associated initiatives intended to reduce social exclusion, particularly in the land use transport arena. Stanley et al. [22,23] developed a monetary measure of the value of an additional trip as it relates to reducing risk of social exclusion. The evolution of that research has subsequently seen monetisation of potential benefits from reducing exclusion risk widened, to include contributions from bridging social capital, bonding social capital, sense of community, three conceptions of wellbeing, as well as neighbourhood disadvantage [24,25]. These various ways of reducing risk of social exclusion are referred to herein as pathways to reduced risk of social exclusion. The current paper extends that research, particularly in relation to how subjective wellbeing is treated, highlighting the importance of understanding who gains and who loses from policy interventions. The paper indicates how resulting monetised values vary across household income levels and suggests how values might be applied, illustrating the substantial potential monetary benefit from improving outcomes on the respective pathway variables, particularly for those from lower-income households. Through demonstration of these monetary values and their significance, the paper aims to encourage take-up of the inferred monetary values in appraisal guidelines, to support policies and other initiatives intended to reduce social exclusion in land use transport settings, with application beyond LUT also possible.
Section 2 of the paper summarises some of the literature relevant to social outcomes in land use transport, particularly around the idea of social exclusion, noting some key pathway opportunities to reduce exclusion risk in relation to social policies and interpersonal intervention. Section 3 briefly describes materials and methods used in the research stream of which this paper is part. Section 4 sets out a model to predict risk of social exclusion and uses that model to infer monetary values associated with changes in several independent (pathway) variables that are significantly associated with exclusion risk. Section 5 discusses the modelling and monetisation work, illustrating the significance of the resulting values for appraisal. Section 6 presents the paper’s conclusions.

2. Literature Review

A good social outcome is described in many different ways, the use of various relevant concepts evolving over time and between disciplines and often used concurrently [26]. This diversity of views is probably both a cause and effect of the relatively underdeveloped state of play in relation to specifying desired social outcomes and thus the frequent absence of a social dimension in project appraisal, noted above. Discussion about social goals was initially often framed around reduction in income-based poverty and disadvantage [27,28] but has broadened over time to incorporate a wide range of potential outcome targets, including concepts such as happiness, wellbeing, flourishing, resilience, social sustainability, social justice, equity, equality of opportunity, capabilities and social inclusion, among others [29]. These components broadly encompass notions relating to access to resources, participation and opportunities [30].
Luz and Portugal [31] (p. 506) refer to Kenyon et al. ’s [32] definition of social exclusion, where it is viewed as a “multidimensional, dynamic and relative” concept which refers to an unequal access to participation in society. This particularly refers to the inability to undertake activities and has the advantage that it is a relative concept and draws attention to equity issues [26,33]. The authors thus suggest that a policy goal of reducing social exclusion offers a useful lens for thinking about societal social outcomes and associated social policy directions for government(s), supporting the achievement of many potential social outcomes.
Being socially excluded can involve many negative aspects for people, communities and society. For example, for the individual, it can threaten the need for belonging, self-esteem and control, which may lead to negative affect, such as depression or anger [34]. Having many people experiencing social exclusion in a community may threaten social cohesion, community strengths and supports, leading to adverse outcomes such as lower productivity. Society is likely to pay a higher cost for higher numbers of people experiencing social exclusion, in terms (for example) of welfare, legal system, economic and health costs [35].
The UK Office of the Deputy Prime Minister [36] listed the following drivers of social exclusion: low income, unemployment, education, ill health, housing, transport, social capital, neighbourhood, crime and fear of crime. Most of these variables have been included in the model considered later in this paper but some were not included because of high correlation with other included variables or a lack of relevant data. The extent of societal inclusion a person has is dependent on a wide range of social conditions a person holds, such as social capital, attachment to community, mobility, income and wellbeing, discussed further below [25]. These conditions are influenced by government policies that facilitate opportunities to gain these conditions, particularly through access to important mediators to achievement, such as education, health services and employment.
Social exclusion is essentially concerned with a person’s capacity to participate in opportunities available in their society, which is increasingly being framed in terms of capabilities [37,38,39]. Barriers to inclusion that are amenable to policy intervention are of particular interest herein. Social inclusion rests on the idea of building a society where people have equal opportunities to build personal capabilities to achieve the outcomes they value [37]. This then allows personal choice as to how a person uses their capabilities. Following the Social Exclusion Unit [40], this broadens the idea of barriers to participation beyond being only a question of income poverty to being a multidimensional concept. Sen (2000) [41], for example, links the concept of social exclusion with capability deprivation, where relational connections are the main characteristic of a social exclusion focus. He notes, for example, how being excluded from social relations can lead to further deprivations.
The substance of capabilities is somewhat elusive in Sen [37], but Nussbaum [38] identifies ten capabilities that she believes are fundamental for people to achieve human dignity and respect. Stanley and Stanley [42] argue that the ability or capacity to be mobile is an intermediate capability that is needed for the achievement of many of Nussbaum’s capabilities. Hickman et al. [39] suggest indicators that may shed light on relative achievement of Nussbaum’s capabilities in a transport setting, to inform assessments of relative equity. Stanley [19] suggests that social inclusion might be seen as an end point of the capabilities framework, with mobility (trips) one intermediate capability that supports achievement. Thus, looking at social inclusion (/exclusion) in this way, social capital, sense of community, wellbeing and area socio-economic advantage/disadvantage can also be framed as contributing to capabilities that support inclusion, hence our argument that social inclusion is a valuable umbrella concept that could be used as an end-point social outcome in need identification and policy/project appraisal.
Turning to the role of mobility, the work of the UK Social Exclusion Unit [40] was vital in bringing the issue of transport and social exclusion to international attention. A range of other studies around that time further added to the knowledge base about connections between travel and social exclusion and/or wellbeing [43,44,45,46,47,48]. These authors often focused on specific at-risk cohorts, particularly older people. The ability to travel has been shown to be important in the establishment of personal relationships, good health, employment and personal growth, life purpose and self-acceptance [44,49,50,51], supporting social inclusion through the achievement of capabilities.
This literature base suggests that, in transport land use settings, social exclusion may result from disadvantages or inequities in one or more of a number of areas, such as socio-demographic circumstances (e.g., age, personal abilities, language, income) [39,40,52,53], transport system characteristics (e.g., availability, accessibility, affordability) [43,54,55,56] and land use/built environment characteristics (e.g., terrain, footpaths, service availabilities) [57,58,59], many elements of which are amenable to policy influence and many of which are apparent through more or less trip-making, social capital or wellbeing or area socio-economic advantage/disadvantage [60,61,62]. Of course, there may be personal issues that impact on a person’s desire to use the capabilities established through mobility policies (or other social policies), with a variable ability to convert resources to outcomes they value [26,31].
While some potential broader societal benefits from reducing mobility-related exclusion have been elaborated [35,63], such as improved health and associated lower health system costs, this work did not encompass monetisation of wider societal benefits. Listing of potential benefit and costs, sometimes rated, is the usual way appraisal guidelines approach social exclusion benefits, if at all, rather than through monetisation [64].
There is an evidence base that monetises some of the factors that are likely to reduce risk of social exclusion, usually developed from modelling of contributions to subjective wellbeing. For example, values for changes in levels of subjective wellbeing have been estimated by van Praag and Ferrer-I-Carbonnell [65], and, in more recent times, HM Treasury and SITF [66], the latter having the gravitas of being associated with UK appraisal guidelines.
There is also research on the monetary value of changes in levels of social capital, typically derived via modelling of contributions to subjective wellbeing (rather than through a social exclusion lens). Trust is the main focus of this social capital work, rather than social capital networks [67,68]. Groot, Van Den Brink and Van Praag [69] have monetised the value of changes in personal network size as one measure of the benefit of social capital. However, they did not distinguish between bridging and bonding social capital networks (that research also inferred monetary values for changes in the extent of a person’s social safety-net and membership of unions or associations), their measure of network size being more reflective of bonding capital than of bridging capital. No monetary values have been found for changes in sense of community, neighbourhood disadvantage or for bridging and bonding social capital separately, other than the values derived in research by the current authors and colleagues.
The most comprehensive attempt to monetise influences on risk of social exclusion in a land-use transport setting has been undertaken by Stanley et al. [22,23,24,25,60,61]. In pursuing monetisation of some factors thought likely to influence social exclusion, Stanley et al. [22] measured what they defined as risk of social exclusion by building on four dimensions developed by Burchardt et al. [33]—consumption, production, political engagement and social interaction—with some modification for Australian circumstances (For example, the Burchardt et al. (2002) [33] indicators included whether someone voted as one indicator, which was removed from the Australian indicators, because voting is compulsory in Australia). Their resulting Australian measure of risk of social exclusion is defined by five dimensions or risk factors, each with an associated threshold.
  • Household income—less than a threshold of AUD 500 gross per week (AUD 2008 prices, when the survey was undertaken), which was the highest level of welfare payment at that time;
  • Employment status—not employed, in education or training, nor looking after family or undertaking voluntary activities;
  • Political activity—did not contribute to/participate in a government political party, campaign or action group to improve social/environmental conditions, or to a local community committee/group in the past 12 months;
  • Participation—did not attend one of the following: a library, sporting or exercise event (participant or spectator), hobby, leisure or interest group, or arts or cultural event in the past month;
  • Social support—not able to get help if you need it from close or extended family, friends or neighbours.
The current paper extends that research by exploring the idea of a subjective wellbeing threshold being needed for inclusion and then showing how monetised values of various factors that influence risk of social exclusion vary by income level. It argues for inclusion of resulting values in appraisal guidelines, to help remedy some of the current appraisal weaknesses relating to social outcomes.

3. Materials and Methods

A number of concepts are important to understanding this subject area. In shorthand terms, the major concepts discussed herein are understood as follows:
  • social exclusion: the existence of barriers which make it difficult or impossible for people to participate fully in their society. Policy-related barriers are of particular interest for this research;
  • accessibility: the ease with which a person, from a particular place, can reach particular services, locations and/or other people;
  • mobility: the ease with which a person moves around (measured here by trip-making);
  • social capital: the benefit a person derives from social networks, trust and reciprocity within a community (or communities), with bridging and bonding social capital associated with social networks the main research focus here [70,71]. While social capital is a widely used term, it remains a controversial concept, as there is no agreed or consistent definition or measurement. Yet, there are agreed concepts around networks, trust and reciprocity and also about the difference between bonding and bridging forms of social capital. The authors have used networks because of the obvious connection between networks and mobility, the main research focus. In addition, it could be argued that trust and reciprocity are more likely to develop if people can meet and form networks (Trust was tried as an additional explanatory variable in modelling but did not contribute significantly to risk of social exclusion. However, trust is highly correlated with bridging (p = 0.035) and bonding social capital (p < 0.001) networks, suggesting that building those networks will support development of trust);
  • transport disadvantage: a situation where people experience a shortage of transport options and/or have restricted ability to use available options, which inhibits their mobility and hence their access to goods, services, places and/or relationships [55];
  • sense of community: the strength of a person’s sense of attachment to their neighbourhood [72,73];
  • wellbeing: a person’s rating of their quality of life. The current paper includes evaluative wellbeing, which is a measure of overall life satisfaction, called subjective wellbeing herein [74,75]. Related research [24] also considers affective wellbeing, which is an assessment of positive and negative emotional states [76,77] (negative affect is not significant in the research modelling of risk of social exclusion and is not discussed further) and eudaimonic wellbeing, which refers to living a life with meaning and purpose, a desire to grow and develop to one’s full potential [50,78]. The focus herein on subjective wellbeing is because of the paper’s intention of exploring the idea of a subjective wellbeing threshold being required for inclusion. Also, subjective wellbeing is the most widely used wellbeing indicator in appraisal/impact assessment settings. Interested readers are referred to Stanley et al. [24] for monetary values of affective and eudaimonic wellbeing.
For modelling purposes, risk of social exclusion is treated as an ordered variable, each respondent having a score between zero and five, depending on the number of thresholds passed/failed. Having five risk factors is worse than having four and four is worse than three but the differences between these pairs of adjacent levels, and others, may not be equal. For modelling purposes in the current paper, those respondents with three or more risk factors (i.e., people likely to be at high risk of exclusion) were aggregated into one group, to keep respondent numbers broadly balanced across the range of risk factors.
Building on past economists’ attempts to monetise benefits and costs of public policies/projects, the analytical starting point for the monetisation work by Stanley et al. (2011) [22] was to assemble a data base that would enable identification of trade-off settings from which monetised values of reduced exclusion risk might be inferred. An Australian Research Council (ARC) Industry Linkage Program Project provided the opportunity to put together a bespoke data base, from which monetisation could be pursued (LP0669046: Investigating Transport Disadvantage, Social Exclusion and Well Being in Metropolitan, Regional and Rural Victoria). Data assembly began with a sample of respondents to a self-completed Victorian government household travel questionnaire. A number of survey respondents were then given the opportunity to opt into an additional comprehensive home-interview survey. People at high risk of social exclusion tend not to complete travel diaries, so a special survey sought such people at places like welfare agency offices, given that social exclusion was a key focus of the research. Modelling results reported in this paper are for the full Melbourne sample (N = 765). Surveys collected information on factors such as trip-making, social exclusion risk factors, social capital, connectedness to community, wellbeing, personality, transport problems, demographics and household composition, using established measurement tools as far as possible. Table 1 below explains how each variable was measured.

4. Results

4.1. Modelling Risk of Social Exclusion

Drawing on the literature summarised in Section 2 and on Stanley et al. [22,24,25] we expected that risk of social exclusion would decrease as the following pathway variables increased: household income, daily trip rate, bridging and bonding social capital, sense of community and subjective wellbeing. Reduction in the level of neighbourhood disadvantage was also expected to reduce risk of exclusion. Most of these independent variables are person-level pathways towards potentially reducing risk of social exclusion but some also involve some interaction with the neighbourhood.
Table 1 shows mean values of key variables and indicates how each variable was measured. The mean number of exclusion risk factors was 1.05/5 from the combined sample, with 72.2% of respondents having only zero or one risk factor, suggesting a high likelihood of inclusion. However, 11.5% had three or more risk factors, suggesting a high risk of exclusion. The proportion with three or more risk factors is similar to the proportion of Melbournians and Australians estimated to be in poverty [79,80], although the social exclusion risk factors used in the current paper are broader than just income (/poverty). Appendix A summarises some further respondent characteristics and compares them to Melbourne as a whole.
Respondent trip rates averaged a little less than four/day. Relative to total possible scores, mean bonding capital scores (18.32/24) were relatively higher than mean bridging capital scores (7.07/12), which underlines the importance of close networks for participants and suggests that bridging capital networks may need attention for greater inclusion. The mean subjective wellbeing score (7.10) is in line with typical Australian levels (https://novopsych.com.au/assessments/well-being/personal-wellbeing-index-adult-5-pwi-a// (accessed on 5 June 2023)).
Modelling undertaken by Stanley et al. [24] used subjective wellbeing as a continuous variable. The authors suggested, however, that some minimum or threshold level of wellbeing may be needed for social inclusion, but this idea was not tested. The current paper undertakes such a test, using the same database as referred to above, with the resulting model then used to infer monetary values for changes in levels of various independent (pathway) variables. Subjective wellbeing scores (measured by the Personal Wellbeing Index PWI) [75] were split into five categories, with cut points at PWI scores of 6, 7, 8 and 9. Table 2 sets out the resulting ordinal logistic regression model. The final model predicts the dependent variable significantly better than the intercept-only model (χ2(16) = 429.87, p < 0.001). The Test of Parallel Lines results indicate that there is not enough evidence to reject the hypothesis of parallel lines, implying the assumption of constant odds ratios is defensible. The McFadden Pseudo R2 measure suggests that the model explains 21.9 per cent of the variability in the dependent variable (recognising that there are questions about the reliability of Pseudo R2 measures with ordinal regression. The coefficients attached to the respective independent variables are the natural logs of the odds ratios attached to those variables.
For the continuous variables, Table 2 shows that risk of social exclusion was significantly negatively correlated (p < 0.05) with household income (per day squared) and daily trip numbers and positively correlated with age. For categorical variables, interpreting the impact of any variable depends on its significance level and on the size and sign of relevant coefficients as compared to the reference level for that variable. Thus, the model in Table 2 shows:
  • a significant association between increased subjective wellbeing and reduced risk of social exclusion but this only applies for those with the lowest levels of subjective wellbeing (i.e., PWI1 = PWI score below six out of 10), supporting the idea that a wellbeing threshold is important for inclusion. The 0.896 co-efficient attached to PWI1 infers that someone whose subjective wellbeing level is in category PWI1 is 2.45 times (95% CI = 1.29 to 4.65) more likely to be in a higher risk level for social exclusion than someone with the highest level of PWI (=PWI5, the reference level for this variable). Alternative cut-points between PWI categories were tested during model development, the significance of this variable typically applying up to a score of around six, so whole number cut-points have been used for ease of interpretation;
  • moving someone from a low to medium or medium to high level of both bridging and bonding social capital reduces risk of exclusion;
  • moving someone to a higher level of sense of community also reduces exclusion risk throughout the category ranges of this variable;
  • risk of social exclusion is expected to be reduced if the socio-economic status of the most disadvantaged postcode areas is increased.

4.2. Monetisation of Main Pathway Variables

The main purpose of this modelling is to infer values for changes in levels of the independent (pathway) variables. Ratios of various (significant) model coefficients from Table 2, relative to the (significant) coefficient for household income per day squared, can be used for this purpose. Because household income is squared, the values for changes in any variable decline inversely with increases in household income, as shown in Table 3.
Differentiating the equation in Table 2 for risk of social exclusion (SOCEX) with respect to trips (to derive the marginal utility of trips = MUTRIPS) and household income per day squared (to derive the marginal utility of income = MUHINC, which includes multiplication by household income), produces an inferred value of an additional trip for someone at that particular household income level. For example, at mean sample household income of $226.33 per day, the following value of an additional trip results:
Marginal utility of trips (MUTRIPS) = −0.060.
Marginal utility of household income per day squared (MUHINC) = 2 × −0.000009446 × $226.33 = −0.00428.
Marginal rate of substitution between trips and household income (the marginal value of a trip) = MUTRIPS/MUHINC (at sample average household income) = −0.060/−0.00428 = AUD 14.05 (rounded to nearest five cents).
For someone from a household whose income equals the sample mean (of AUD 226.33/day in 2008 prices), the inferred value of an additional trip is thus AUD 14.05. However, the distribution of individual household incomes across the sample is such that, on average across the whole sample, an additional trip is worth AUD 21.50 (in AUD 2008 prices). For someone on half mean sample household income, an additional trip has an inferred value of ~AUD 28/trip, whereas it reduces to ~AUD 7/trip for someone whose household income is double the sample household mean. This shows the importance of identifying the beneficiaries/losers from any policy intervention. For example, if a new bus service is being planned, identifying prospective service users and their household incomes would enable application of relevant trip values in an evaluation of the case for providing the new service, within a cost-benefit framework.
In terms of other potential pathways to reducing exclusion risk, Table 3 suggests that, for a respondent whose household income is equal to the sample mean, increasing bridging social capital levels from low to medium levels is valued at around AUD 126/day (in AUD 2008 prices), or a higher AUD 150/day if going from medium to high levels, but these values are considerably higher for someone from a low income household. For bonding capital, an increase from low to medium levels is valued at AUD 186 and a lower AUD 108/day for going from medium to high, for someone from a household with sample mean household income. These relativities between bonding and bridging capital values are what we expected, mirroring the idea that bridging capital is mainly about ‘getting ahead’ and bonding capital about ‘getting by’. Table 3 further indicates that values for increasing sense of community are higher in going from a medium to high level than from a low to medium level, suggesting that this is more valuable once other life priorities are handled. All these monetary values can be updated to current price levels for application.
Increasing someone’s level of subjective wellbeing from the lowest to second lowest level is valued at AUD 148/day (not shown in Table 3), for a respondent with mean sample household income. However, that is a misleading indicator of the likely monetised value, since those with the lowest levels of PWI have lower mean household incomes. Thus, while the mean household income across the full sample was AUD 226.33/day, for those with the lowest level of subjective wellbeing (PWI1) it was AUD 181.43/day (AUD 2008). Variations between mean household income levels across higher levels of PWI were much smaller than between the two lowest levels of PWI. When mean household income across PWI levels is taken into account, the value of taking someone from PWI1 (PWI score up to six) to PWI2 (PWI above six) increases from AUD 148/day to AUD 184/day. This higher value is relevant in application.
If PWI had been modelled as a continuous variable, then the inferred value of a unit change in PWI becomes AUD 72/day across the full PWI range, for a household with mean sample household income, or AUD 111/day as a mean value across the full sample, taking individual household incomes into account. The latter is the more appropriate value to represent the whole sample, providing a useful basis for benchmarking against other research, s discussed in Section 5. The two ways of incorporating PWI (categorical versus continuous) are similar in terms of model fit, the categorical formulation being consistent with the idea of a PWI threshold being needed for achieving social inclusion (in terms of taking someone to a PWI score from six or below to above six, higher PWI values not being significant in the model).

5. Discussion

If the monetary values in Table 3 are to be used in policy, program and/or project appraisal, there needs to be some assurance that they are reliable. Given that, to the best of our knowledge, no-one else has estimated such a comprehensive set of monetary values for factors associated with risk of social exclusion, what evidence can be provided to support application?
Values for changes in subjective wellbeing have been estimated by others, providing one way to benchmark values in the current research. HM Treasury and SITF [66] suggest a mid-range value of GBP 13,000 for a one unit increase in a person’s subjective wellbeing score for one year in 2019 prices (called one ‘Wellby’), with a low value of GBP 10,000 and high value of GBP 16,000. Mean UK household income in that year was GBP 35,900 (Average household income, UK—Office for National Statistics (ons.gov.uk)), suggesting that one Wellby was equal to around 36% of mean household income (28–45% for the low/high values). To benchmark the subjective wellbeing values derived in this research stream, they can be expressed as a proportion of household income and then compared to the UK proportions. Stanley et al. [25] suggested a mean value range of AUD 97–124/day per unit change in PWI and the mean sample value for PWI as a continuous variable (as estimated in background research for the current paper) is AUD 111, mid-range in the Stanley et al. [25] range. This implies a PWI value range of 43–55% of sample daily mean household income, which intersects the UK range at its high end but is below what Biddle et al. [81] found in an Australian analysis.
These comparisons provide comfort about the magnitude of wellbeing, and, by extension, other monetised values estimated herein, particularly given that the trade-off settings used in the current research to monetise changes in subjective wellbeing are different to those used in the UK wellbeing valuation and in Biddle et al. ’s [81] research. The UK approach draws on an association between life satisfaction and the value of a Quality Adjusted Life Year (low value) and on association between life satisfaction and income (high value) (Fujiwara and Dass 2021) [82]. Biddle et al. [81] also used an income–life satisfaction approach. In contrast, the current research has used risk of social exclusion (as defined and measured) as its dependent variable for exploring wellbeing and other pathway values and has produced comparable findings in terms of PWI values, relative to mean household income, to the UK and Australian research cited.
Trip values estimated in this research are considerably higher than would have resulted if the traditional economists’ approach to valuing benefits from additional travel had been used: the ‘rule of a half’. The higher monetised value is because increased trips represent a non-marginal change in personal activity levels (when people average around 3–4 trips a day, one additional trip is a 25–33% increase, which represents a substantial increase in daily activity levels), unlike the marginal changes that typically result from transport improvements (e.g., small savings in travel time). Additionally, in accord with the idea of merit goods, the trip value estimated herein is interpreted as reflecting both (1) the value of increased activity to the trip maker (the user) and (2) some of the benefits society receives when social exclusion is reduced (e.g., benefits from better health and lower crime). The resulting values are regarded as estimates of the societal willingness to pay for mobility as a merit good (SWTP). An individual preference approach will under-value the benefits of merit goods, as application of the ‘rule of a half’ in this setting demonstrates.
The high value of trips in contributing to reduced risk of social exclusion, particularly for those from lower income households, has a number of significant implications for transport policy. Three examples that follow illustrate this significance: for local public transport services, including establishing threshold service levels for inclusion; for setting priorities between measures that reduce trips versus those that reduce trip lengths and/or modal choices; and for ‘competition in the market’ as a means of providing public transport services.
A recent case study applying the trip values derived by Stanley et al. [25] shows that the resulting benefits can be very large indeed, accounting for one-third of total monetised benefits from a hypothetical doubling of bus services in Sydney’s west, a relatively disadvantaged part of Sydney, and over half of the estimated benefits from building the Parramatta Light Rail in Sydney’s west [61]. Benefits of this potential magnitude make a material difference to appraisal outcomes in CBA. To put them in a broader context, they are comparable to, if not larger than, the scale of wider economic benefits (WEBs) from some major transport initiatives, which have been counted in many transport CBAs (when relevant) for over a decade. For example, wider economic benefits accounted for around 40 per cent of total benefits from a 2011 appraisal of London’s CrossRail 1 project, a relatively high proportion for this benefit category [83]. Inclusion benefits in the two Sydney public transport case studies in Stanley et al. [61] are of a similar scale. WEBs are usually much smaller than 40 per cent, estimated (for example) at an 8% mark-up on conventional user benefits for Sydney’s North-West Rail Link by Legaspi, Hensher and Wang [84]. This suggests that inclusion benefits, at scale, are potentially an even more significant inclusion in CBA than WEBs. This significance should increase implementation prospects for relevant land-use transport measures intended to reduce exclusion risk. This should, in turn, lead to increased inclusion and greater resulting opportunity equity in land transport. This should also lead to re-examination of the high expenditure on mass transit and freeway developments, such as currently underway by the Victorian Government, where a full appraisal may reveal relatively higher benefits would result from expenditure on social transit.
Cities such as London and Vancouver are pursuing transport opportunity equity in their city planning largely by ensuring that there is a threshold public transport service level available across the city, supported by building strong communities [19]. The trip values derived in the current research stream can assist the development of such public transport service thresholds, since they support the process of quantifying associated inclusion benefits. Australian application, for example, has suggested minimum boarding rates of 6–7 per service hour are needed for a regional urban route bus service to generate inclusion benefits of a similar scale to service costs or 10–11 per service hour in Melbourne [15,85].
In terms of developing packages of policy measures to reduce the external costs of road transport, the high value of trips calculated from this research suggests the focus should be on reducing trip lengths, such as by increasing local activity densities (improving accessibility) and changing modes to low impact modes, rather than on measures that lead to reductions in absolute trip numbers, a point being made in the literature [8,26]. Trips have value, usually due to the activities they enable. Making the achievement of that value easier, by reducing trip lengths or increasing the attractiveness of alternative modes for undertaking the trip, adds value without risking loss of the value of the activity associated with the trip. Such an approach is also likely to improve all three sustainability goals: social, economic and environmental.
The demonstrated benefit of trip-making as a means of reducing exclusion risk poses questions for some current transport policies, such as the UK policy of providing public transport over much of the country by reliance on ‘competition in the market’, where service provision depends essentially on the financial viability of a service to its provider. The demonstrated high value of trip-making derived herein suggests that the ‘competition in the market’ model can be expected to be regressive: services will be provided where users can pay, rather than where the societal benefits to at-risk people and wider society are likely to be substantial but fare revenues much less so. As a result, Stanley et al. [8] argue for governmental financial support for transport initiatives intended to generate inclusion benefits. Such benefits are largely ignored under ‘competition in the market’, except to the extent some passengers may receive fare concessions and/or operators be provided with some financial assistance to support their operations (e.g., fuel tax rebates or subsidies).
Beyond the value of trips, Table 3 includes some other monetised values that may be applicable to a CBA of initiatives directed at reducing risk of social exclusion in an urban LUTP setting. In evaluations where before and after estimates for social capital (bridging and/or bonding), sense of community and subjective wellbeing are available (in most cases, highly unlikely), then the values from Table 3 for those variables would be relevant, for specific household income bands. By way of illustration, COVID-19 has encouraged greater working from home in many cities. This should be supportive of the growth of stronger communities, a core planning focus in cities such as London and Vancouver [10,11]. Benefit measures such as for local trip-making, social capital (especially bridging social capital), sense of community, subjective wellbeing and neighbourhood disadvantage are all relevant (partial) indicators of strong communities. Initiatives to build stronger communities should be expected to see gains in some or all these performance indicators. Before and after studies can be used to assess how particular initiatives change one or other of these indicators at community or neighbourhood level (as applicable to the circumstances under consideration). The relevant unit benefit values derived herein, taking account of household income levels of affected people, can then be applied to the associated indicator changes, to produce monetary benefit measures, post implementation. Repeated before and after applications should then enable unit benefit values to be increasingly used in pre-implementation appraisals, as dose-response understanding of impacts on key variables accumulates. This broadens opportunities for demonstrating the benefit values associated with building stronger communities.
The paper underlines the importance of social capital for inclusion and the values of increasing bonding and bridging capital. Building bonding social capital through improving mobility can be seen as particularly important for those on a low income. The Broaden and Build psychological theory of Fredrickson [86] suggests theoretical support here, through broadening thought-to-action, or activities that build personal resources, including drawing on capabilities and social connections that offer emotional support or bonding social capital.
The various pathways to reducing exclusion discussed herein have been explored, and values estimated, in a land-use transport setting. However, the authors are about to use some of the values in question to quantify potential benefits of nature-based treatments for people with mild mental illness, showing wider opportunities for application. No such monetisation has been applied in that application area to date, to the best of the authors’ knowledge.
The surveys that provided the evidence base for this monetisation research were undertaken about 15 years ago. Use of information and communications technologies (ICT), including social media platforms, has become much more widespread since that time (for example, the number of world-wide social media users increased by half between 2018 and 2022, according to Statista. Number of worldwide social network users 2027|Statista), including travel-based ICT use. This poses the question of whether the growth in ICT use may have reduced the need for and, by implication, value of trips, as estimated herein.
Urry [87] (p. 255) argues that “… virtual travel will not in a simple sense substitute for corporeal travel, since intermittent co-presence appears obligatory for many forms of social life”, suggesting the values derived herein are likely to survive the ICT challenge. If ICT use is reducing the need for trips (/travel), then a decline in average trip rates over time would be expected. In the decade since 2008, however, National Travel Survey data suggest that personal trip rates in the UK (for example) have been largely stable, averaging only 2 per cent less over calendar years 2017 and 2018 (pre-COVID) than in 2007 and 2008 (nts0101.ods (live.com (accessed on 10 May 2023)). This suggests that increased ICT use is not substituting for trips and their associated activities, in aggregate, but may be enabling additional work or leisure activities to be pursued while travelling [88,89]. The net effect of ICT seemingly (1) not substituting for trips or, by implication, for activities enabled by such trips but (2) potentially adding to the value that the trip-maker can realise from time spent in travel (reducing the disutility of travel) might thus be that the net value of trips as derived herein increases somewhat with greater ICT use, through the increased opportunity that ICT enables to engage in chosen activities. Exploring such questions should be a research priority.

6. Conclusions

Reducing social exclusion is currently high on the policy priority lists of many governments. In jurisdictions where CBA is used to help inform policy/project decision-making, monetisation of potential benefits and costs is desirable, but such monetisation has been lacking for initiatives intended to reduce social exclusion. This is a particular problem for social transit improvements, by way of example. The paper has affirmed previous work by Stanley et al. [25], showing how monetary values can be derived for a number of factors found to influence risk of exclusion, these influencing factors (or pathways) being trips, bridging and bonding social capital, sense of community, subjective wellbeing and neighbourhood disadvantage. It has extended that prior research by showing that there are grounds to argue that a subjective wellbeing threshold needs to be achieved to reduce exclusion risk. The paper has also demonstrated how the monetary value of those various pathway influencers of risk of social exclusion are considerably higher for those from lower-income households, which argues for policies intended to reduce exclusion focusing closely on initiatives to benefit this group. Importantly, application of these monetary values in relevant policy/project evaluations of exclusion-reduction initiatives should help to strengthen the case for implementation and go some way to removing the bias against such initiatives in current appraisal guidelines.
To support implementation of initiatives to reduce exclusion risk, inclusion of at least the trip values derived herein in governmental Appraisal Guidelines is important, as a starting point. Given that this research stream appears to be the first international research evidence to estimate monetary values of trips based on reducing exclusion risk, their inclusion in Appraisal Guidelines should realistically begin in the form of what UK Guidelines call Indicative Monetised Impacts. This might involve (for example) sensitivity testing of the effect of including/not including this trip benefit value on an initiative’s benefit/cost ratio and identification of switching values, at which the benefit item changes an initiative’s value for money rating.
Incorporation of other monetised pathway values derived herein in appraisal guidelines, as Indicative Monetised Values, would also support their application and, importantly, should encourage more integrated approaches to reducing exclusion. Before-and-after studies can be used, for example, to broaden the evidence base to support inclusion of values for bridging and bonding capital and sense of community. The subjective wellbeing values derived herein benchmark well against other such values, which is encouraging in terms of the plausibility of the other pathway values set out and supports early application of those values in appraisal guidelines.
The differences between the monetised values of different pathways to reducing risk of social exclusion at different household income levels underlines the importance of knowing who is affected by land use transport policies and planning and other initiatives to reduce exclusion. The inferred monetary values suggest that, in terms of reducing risk of social exclusion, there is much merit in focussing on those with low starting levels of subjective wellbeing and/or low levels of social capital and/or low household income.

Author Contributions

Conceptualisation, J.S. (John Stanley); Methodology, J.S. (John Stanley) and J.S. (Janet Stanley); formal analysis, J.S. (John Stanley); Investigation, J.S. (John Stanley) and J.S. (Janet Stanley); Writing—original draft preparation, J.S. (John Stanley) and J.S. (Janet Stanley); writing—review and editing, J.S. (John Stanley) and J.S. (Janet Stanley). All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The original research on which it builds received a grant from the Australian Research Council (LP0669046: Investigating Transport Disadvantage, Social Exclusion and Well Being in Metropolitan, Regional and Rural Victoria).

Institutional Review Board Statement

Ethics approval was given by Monash University (2008000034—CF08/01 62).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study survey.

Data Availability Statement

The authors will respond to personal requests for data access.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Respondent Characteristics Compared to Metropolitan Melbourne.
Table A1. Respondent Characteristics Compared to Metropolitan Melbourne.
CharacteristicsSample (N = 765)
(Labour Force) (%)		Melbourne Statistical Division
(Labour Force) (%)
Labour force status
Full-time18.1 (37.0)42.2 (64.9 a)
Part-time or casual21.4 (43.8)19.2 (27.7 a)
Unemployed9.4 (19.3)3.7 (5.3 a)
Retired20.210.4 b
Study16.75.8 b
Home duties5.710.4 b
Age
15–1717.14.8
18–3925.640.7
40–6438.438.7
65+18.915.8
Education
Some primary school0.5Na
Finished primary school0.5Na
Some secondary school43.2Na
Finished secondary school15.217.2
Diploma/Certificate20.422.3
Degree11.518.5
Post-graduate8.79.0
Country of birth
Australia77.264.1
English speaking8.715.2
Non-English-speaking country14.220.7
Notes: a = As a percentage of those aged 15 and over in the Labour Force; b = As a percentage of those aged 65 or over. Source: Survey responses; Australian Bureau of Statistics 2006 Census of Population and Housing General Community Profile, Cat. 2001.0. Canberra: Author. https://quickstats.censusdata.abs.gov.au/census_services/getproduct/census/2016/communityprofile/2GMEL?opendocument (accessed on 10 March 2012).
Table A1 summarises some survey participant characteristics and compares them to the population of Melbourne Statistical Division (MSD). The latter data are from the 2006 Australian population census, about one year prior to the time of the surveys. Some data comparisons are not available. Survey participants generally were less involved in full-time employment than the Melbourne labour force as a whole, more likely to be unemployed and had lower educational attainment. This reflects the survey’s interest in social exclusion. The sample population had a much higher proportion of youth (15–17 years of age) than Melbourne, reflecting the survey’s intention of exploring mobility challenges confronting young people. Survey representation of those aged 40 or over is comparable with the wider Melbourne population but respondent numbers aged 18–39 are well below the Melbourne proportion. Overseas-born were considerably under-represented in the sample (by 13 percentage points). In terms of wider application of the monetary values calculated in this research, it is not clear how this sample selection would influence the monetary values derived in the analysis. Australia has a relatively high component of skills-based and business immigration, and a strong family re-union and smaller refugee program, where the influence on monetary values of wellbeing change might operate in different directions.

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Table 1. Mean values for key variables for modelling social exclusion risk: Melbourne 2008 (N = 765).
Table 1. Mean values for key variables for modelling social exclusion risk: Melbourne 2008 (N = 765).
VariableUnitsSample Means (N = 765)
Social exclusion risk factors0 to 5 a1.05
TripsNumber/day3.66
Bonding capitalIndex b18.32
Bridging capitalIndex c7.07
Sense of communityIndex d57.61
Personal (subjective) wellbeingIndex e7.10
Household incomeAUD 2008/day226.33
Notes: a = index based on number of exclusion risk thresholds (out of 5) failed, these thresholds relating to household income, employment status, political activity, participation and social support, as explained in the literature review section above. b = Index derived from four 6-point scales, relating to frequency of contacts (Never = 1; Less than Once a Year = 2; More than Once a Year = 3; About Once a Month = 4; About Once a Week = 5; Most Days = 6) within close networks (members of your close family; members of your extended family; friends/intimates; neighbours). Possible scoring range = 4–24. For modelling, this variable was converted to a categorical variable by setting cut points between low/medium and medium/high ranges at scores of 17 and 20, to give approximately equal numbers of respondents in each category. c = Index derived from two 6-point scales, relating to frequency of contact (same as in note ‘b’) with wider networks (work colleagues; people associated with groups in your community). Possible scoring range = 2–12. For modelling, this variable was converted to a categorical variable by setting cut points between low/medium and medium/high ranges at scores of 5 and 9, to give approximately equal numbers of respondents in each category. d = Index derived from twelve 7-point scales (Sense of Community Scale) [72]. Possible scoring range = 12–84. For modelling, this variable was converted to a categorical variable by setting cut points between low/medium and medium/high ranges at scores of 54 and 63, to give approximately equal numbers of respondents in each category. e = Index derived as average of eight 10-point scales (Personal Wellbeing Index) [75]. Treatment for modelling is discussed in the following paragraphs.
Table 2. Modelling social exclusion risk (dependent variable): Melbourne 2008 data.
Table 2. Modelling social exclusion risk (dependent variable): Melbourne 2008 data.
AttributeUnitsCombined Sample (N = 765)
Age (continuous variable)Years0.016 (<0.001)
Number of Trips (continuous variable)Trips/day−0.060 (0.034)
PWI1 (cat. var.)1.00.896 (0.006)
PWI2 (cat. var.)1.00.264 (0.410)
PWI3 (cat. var.)1.00.008 (0.980)
PWI4 (cat. var.)1.0−0.111 (0.727)
Sense of Community Low (cat. var.)1.00.824 (<0.001)
Sense of Community Medium (cat. var.)1.00.508 (0.008)
Bridging Capital Low (cat. var.)1.01.177 (<0.001)
Bridging Capital Medium (cat. var.)1.00.640 (<0.001)
Bonding Capital Low (cat. var.)1.01.255 (<0.001)
Bonding Capital Medium (cat. var.)1.00.461 (0.017)
Household Income Day Squared (cont. var.)AUD/day−0.000009446 (<0.001)
SEIFA Low (cat. var.)1.00.536 (0.004)
SEIFA Med (cat. var.)1.00.254 (0.144)
Sample Type (cat. var.)1.0−1.115 (<0.001)
Threshold parameters
Mu (01) 0.421 (0.329)
Mu (02) 2.614 (<0.001)
Mu (03) 4.203 (<0.001)
Model fitting information (−2 Log Likelihood) <0.001
Test of Parallel Lines 0.202
McFadden Pseudo-R2 0.219
Table 3. Inferred monetised values of various pathways to reducing risk of social exclusion, as household income changes (Melbourne; AUD 2008 values).
Table 3. Inferred monetised values of various pathways to reducing risk of social exclusion, as household income changes (Melbourne; AUD 2008 values).
Household Daily IncomeBonding Social CapitalBridging Social CapitalSense of Community
Low to MediumMedium to HighLow to MediumMedium to HighLow to MediumMedium to High
80525305355423209336
160263152178212104168
32013176891065284
480885159713556
Sample mean = 22618610812615074119
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Stanley, J.; Stanley, J. Improving Appraisal Methodology for Land Use Transport Measures to Reduce Risk of Social Exclusion. Sustainability 2023, 15, 11902. https://doi.org/10.3390/su151511902

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Stanley J, Stanley J. Improving Appraisal Methodology for Land Use Transport Measures to Reduce Risk of Social Exclusion. Sustainability. 2023; 15(15):11902. https://doi.org/10.3390/su151511902

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Stanley, John, and Janet Stanley. 2023. "Improving Appraisal Methodology for Land Use Transport Measures to Reduce Risk of Social Exclusion" Sustainability 15, no. 15: 11902. https://doi.org/10.3390/su151511902

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Stanley, J., & Stanley, J. (2023). Improving Appraisal Methodology for Land Use Transport Measures to Reduce Risk of Social Exclusion. Sustainability, 15(15), 11902. https://doi.org/10.3390/su151511902

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