Willingness to Volunteer: An Exploration of the Potential Volunteer Pool for Community Arts Programs

Abstract

Volunteers are critical for many local arts and culture programs. In contrast to most research, we focus on potential rather than actual volunteers. Using data collected from an online survey of 948 participants, we explore the extent to which individuals are willing to contribute both their time and money to support community arts initiatives. Results from a binary two-stage instrumental variable (IV) probit indicate that a significant predictor of willingness to volunteer is the willingness to pay and the intangible value placed on arts activities and engagement. These findings have implications for recruitment initiatives by public and community arts organizations reliant on volunteer support.

Keywords

willingness to contribute willingness to volunteer volunteering potential volunteers community arts and culture

Introduction

The contribution of volunteers in supporting the arts and culture sector is well-documented (Bendle & Patterson, 2008; Saleh & Wood, 1998; Stebbins, 2013; Trivic et al., 2020). In this note, in contrast to many previous studies, we focus on potential rather than active volunteers and consider the potential pool of volunteers that exists. Just as collaboration associated with “prosumers” in artistic and cultural projects has morphed traditional distinctions between consumers and producers (see Nakajima, 2012; Nolan, 2021; Ritzer & Jurgenson, 2010), we assert that this concept extends to volunteers in the arts as well.

In community art programs, volunteering provides a source of labor supply often vital to projects being realized, as well as means to harness volunteers’ pro-social motivations and prosumption, where individuals act as both consumers and producers (Trivic et al., 2020). We believe that viewing volunteers as a source of labor and a consumer of art enables a better appreciation of their contribution—particularly in the context of community arts programs.

In this exploratory study, we draw upon Klamer’s (2016) theory of willingness to contribute. The desire to contribute to the arts, both in terms of time and money, is driven by the desire to be close to the experiential benefit (Klamer, 2016). Beyond generating value for those directly involved in the production and consumption of art, Klamer argues that engagement contributes to societal values such as connecting local communities and building social cohesion.

This preliminary study surveys a representative sample of citizens drawn from across metropolitan Melbourne, Australia. Two main research questions are addressed, as follows:

Research Questions 1: What are the characteristics of potential volunteers associated with their willingness to donate time and/or money to support community arts programs?

Research Questions 2: Do attitudes about the arts and culture and/or current levels of cultural values influence potential volunteers’ willingness to donate time and/or money to support community arts programs?

We break down the willingness to contribute into two constituent parts (Willingness to Pay [WTP] and Willingness to Volunteer [WTV]) that enables each part to be considered. In this study, we focus on potential volunteers and their WTV. We estimate WTV with a binary two-stage instrumental variable (IV) probit model. The analysis controls for demographic characteristics along with attitudes concerning arts and culture. It also examines whether there is a positive association between WTP and WTV.

Our results are consistent with existing literature (e.g., Apinunmahakul et al., 2007; Brown & Lankford, 1992), suggesting that willingness to contribute both time and money are complements rather than substitutes. It also builds on the works of Bussell and Forbes (2007) and Gross and Rottler (2019), recognizing underlying drivers that play a role in motivating arts volunteers. Furthermore, insights from the findings to aid volunteer recruitment align with Haski-Leventhal et al. (2018) by providing an understanding of the untapped “volunteer energy,” which may help with recruitment strategies.

Determinants of Volunteering

Several studies have profiled volunteers, providing insights into volunteer segments in a diverse range of organizations (Bussell & Forbes, 2002; Dolnicar & Randle, 2007; Gray et al., 2012; Nesbit et al., 2018; Stukas et al., 2014). In addition, differences between volunteer and non-volunteer motivations, and measures designed to overcome various barriers have also been investigated (Clary et al., 1996; Haski-Leventhal et al., 2018; Ziemek, 2006). We briefly focus on three key determinants most relevant to this study.

The Role of Socio-Demographics

Desirable pro-social behaviors such as charitable giving and volunteering are often linked and there is extensive literature exploring common sociodemographic characteristics these share. Several studies exploring both behaviors find that giving time, and also money tends to increase with age, education, and income (e.g., Bekker & Weipking, 2011; Wilson, 2000; Zarzuela & Antón, 2015). Gender differences also exist with females more likely to volunteer and give money (Bekkers, 2010; Einolf, 2011). Interestingly, Wilson (2012) finds that gender differences influence the number of hours and the volunteering domain.

Studies on age and volunteerism are less in agreement. Bussell and Forbes (2002), along with Gray et al. (2012), report that individuals aged above 50 years are more likely to volunteer compared with younger people including youths. In contrast, Lyons et al. (2006), and also Warburton and McDonald (2009), report that older individuals are less likely to volunteer; however, those who do volunteer are more likely to contribute many hours and demonstrate a high degree of commitment to the organizations they serve. Gray et al. (2012) identify a U-shaped relationship between age and volunteerism but caution that the relationship is dependent on the type of community organizations volunteers are involved with. They assert that volunteering with religious groups, the environment, animal welfare, alongside the arts and culture, tends to be higher for younger and older adults and lower among those in middle adulthood.

Einolf (2011) finds that part-time workers are more likely to volunteer. Complementary to this, Bekkers (2010) asserts that the opportunity cost associated with volunteering is highest for those in full-time employment. Notably, Apinunmahakul et al. (2007) argue that employed people view time and money as complements when giving to support a cause.

Values and Attitudes

Understanding the nature of human cooperation is an important objective in many research domains. The causes that spur individuals to volunteer and demonstrate their pro-social motives are shaped by their underlying social and cultural values and attitudes (Bogaert et al., 2010). While this is hardly an unexpected revelation, its significance at a practical level for organizations that depend upon volunteer support is frequently overlooked with implications for volunteer recruitment and retention (see Bruyere & Rappe, 2006).

The alignment of personal values with a particular cause has been described by Wymer (1997) as a necessary (although not a sufficient condition) to understanding individuals’ decision to volunteer, as a way to demonstrate support for particular causes that matter to them, including the arts and culture. As Dolnicar and Randle (2007) suggest, cultural and social values can enable organizations to better understand their volunteer pool so that they can effectively adopt segmentation and positioning tactics to source and utilize volunteers more successfully. Meanwhile, other research (Gray et al., 2012; Lwin et al., 2014; Nesbit et al., 2018; Shelley & Polonsky, 2002) finds that the sector that the organization is linked to also plays an important role in determining the level of support offered by volunteers.

Volunteerism in the Arts

Positive emotions and group bonding have been associated with motivation to volunteer and donate to arts organizations (Paxton et al., 2020). Furthermore, social psychologists have linked the satisfaction of primary psychological functions with the act of volunteering (Clary et al., 1996; Stukas et al., 2014). As an example, volunteers can be motivated out of a desire for self-expression and to act upon values that are important to them. Literature in arts management finds that volunteers in the arts sector are driven by a personal interest in culture and the arts (see Deery et al., 2011; Gross & Rottler, 2019; Wymer & Brudley, 2000). The motivations of arts volunteers can be differentiated from other non-arts volunteers, with Wymer and Brudley (2000) finding that arts volunteers tend to place higher values on the pursuit of an exciting life, along with a world of beauty and freedom. Interestingly, these values also align with the process of making art and being an artist, hence suggesting that volunteers in the arts that look to these artistic ideals may be more self-orientated (Gross & Rottler, 2019) and less motivated by altruism. Bussell and Forbes (2007) find that personal encounters with existing volunteers are important for recruitment in the specific case of the theater, whereas Gross and Rottler (2019) extend the importance of these word-of-mouth strategies to a wider group of arts organizations, including galleries and museums. Similarly, volunteers may be motivated by their desire for specialist knowledge akin to using their time spent volunteering as “serious leisure” (see Deery et al., 2011; Orr, 2006; Stebbins, 1996). In other research involving the community arts sector, Warren (2014) asserts that volunteers are often motivated in the goal to create or contribute to a shared creative local identity.

Data and Methodology

Data

We examine self-reported WTV for community arts programs offered at a municipal level by participants who have not recently engaged with any of the art programs. Data for the study were collected through an online survey as part of a commissioned project.¹

The survey screened for local residence and noninvolvement with the target arts programs in the past 12 months. Participant demographics, gender, and age group were also collected at screening, with participants admitted until census-derived age-gender quotas for the metropolitan region were fulfilled corresponding to a sample size of 1,000. In total, 1,402 participants were screened for eligibility, and 24 (1.7%) did not complete the screening questionnaire. Of the remaining 1,378 participants, 987 (71.6%) were eligible for the survey, and 948 completed the survey in full. Respondents were blinded to the survey, until after screening, to prevent the risk of self-selection into the topic of the survey.

Table 1 reveals that close to one in five individuals indicated they would be willing to volunteer to support the target organization’s arts programs, while nearly 28% said they would contribute some money to support the organization’s art programs. As per demographic quota, gender distribution was even (51% female) while participant’s ages were not dominated by any particular bracket and, similarly, household income brackets suggest that the sample was indicative of the broader population from which it was drawn.

Table 1.

Descriptive Statistics of the General Public Survey Response, n = 948.

Variable	N	Mean/freq.	SD/%
Volunteer
No	948	677	49.49
Yes	948	271	19.81
Willingness to pay
AU$0	948	688	72.19
AU$10–AU$50	948	148	15.53
AU$51–AU$75	948	42	4.407
AU$76–AU$150	948	44	4.617
AU$151–AU$500	948	13	1.364
Above AU$500	948	13	1.364
Age group
18–24 years	948	103	10.4
25–34 years	948	165	16.67
35–44 years	948	187	18.89
45–54 years	948	177	17.88
55–64 years	948	144	14.55
65 years and above	948	172	17.37
Language other than English at home
Yes	948	199	20.93
Combined annual household income
AU$15,000–AU$30,000	948	119	12.53
AU$31,000–AU$60,000	948	196	20.63
AU$61,000–AU$100,000	948	208	21.89
AU$101,000–AU$150,000	948	130	13.68
AU$151,000–AU$200,000	948	71	7.474
AU$201,000–AU$250,000	948	22	2.316
Above AU$250,000	948	17	1.789
Prefer not to answer	948	148	15.58
Employment status
Unwaged	948	53	5.579
Studying full-time	948	66	6.947
Not currently working	948	106	11.16
Retired	948	188	19.79
Education
High school or less	948	304	32.07
Certificate or diploma	948	265	27.95
Higher education	948	379	39.98
Gender
Male	948	446	45.05
Female	948	502	50.71

Note. All price terms are expressed in current Australian Dollar (AUD) terms.

Contingent Valuation Method

Contingent valuation has been used to measure support for the arts from the perspective of both users and nonusers (Noonan, 2003). As an approach to measure the willingness to contribute comprised both WTP and WTV, the survey included a hypothetical scenario involving the loss of funding and support that would mean a reduction in art programs² (see Elkins et al., 2017). After being presented with a hypothetical scenario, respondents were asked to first identify a level of monetary contribution that they would be willing to make, and then any time contributions as volunteers, to support current levels of art activity. For this, we employed a multistep approach to ameliorate biases, including grounding bias and exaggeration bias, in eliciting both WTP and WTV. Instead of revealing the full range of possible responses, a dichotomous response format was used with “yes” and “no” responses to incrementally increasing payment and number of hours thresholds.

Measuring attitudes

In addition, we also measured participant’s attitudes toward the arts and their engagement in arts and culture–related activities. To measure cultural attitudes, we used the dimensions established by Throsby (2001) and previously applied by Chang and Mahadevan (2014). Statements related to six cultural dimensions were presented where respondents could agree or disagree using a Likert-type scale. The statements were designed to capture the values and attitudes of respondents toward the arts and culture, and are presented in Table 2.

Table 2.

Summary of Respondents’ Value Dimensions Concerning the Arts and Culture.

Value dimension	Survey statement	Average factor loading
Educational value	Arts events and performances contribute to the cultural knowledge and education	0.834
Direct value	The contribution of the City of Melbourne’s artists helps foster a culture of creative thinking and innovation that benefits Melbourne more generally	0.857
Prestige value	The arts are important to the City of Melbourne’s national and international profile	0.899
Bequest value	The City of Melbourne’s iconic venues, arts festivals, and arts culture should be preserved for the future	0.876
Indirect value	Arts events contribute to the local economy by attracting visitors who also spend their money at local businesses	0.845
Existence value	The Council for the City of Melbourne has a responsibility to support the arts and culture to benefit Melbourne more broadly	0.859

Further to respondents’ attitudes toward the arts, we were interested in how their level of cultural participation and engagement across a range of activities might impact their willingness to contribute to support the arts. As such, we replicated within the survey questions sourced from the Australian Bureau of Statistics Cultural Participation Survey (Australian Bureau of Statistics [ABS], 2012). A descriptive summary of participant’s engagement in cultural activities is presented in Table 3.

Table 3.

Summary of Respondents’ Cultural Participation and Engagement.

Variable	M	SD	Min	0.25	Quantiles	0.75	Max
Variable	M	SD	Min	0.25	Mdn	0.75	Max
Arts galleries	1.78	1.01	1	1	2	2	7
Museums	1.85	0.91	1	1	2	2	6
Zoological parks and aquariums	1.92	0.93	1	1	2	2	7
Botanic gardens	2.16	1.14	1	1	2	3	7
Libraries	2.95	1.94	1	1	2	4	7
Archives	1.25	0.78	1	1	1	1	7
Performing arts	1.67	1.08	1	1	1	2	7
Classical music concerts	1.34	0.82	1	1	1	1	7
Popular music concerts	1.78	1.07	1	1	1	2	7
Theater performances	1.71	1	1	1	1	2	6
Dance performances	1.39	0.86	1	1	1	1	7
Musicals and operas	1.55	0.93	1	1	1	2	7
Other performing arts	1.4	0.87	1	1	1	1	7
Heritage sites	1.78	1.07	1	1	1	2	7
Cinemas	3.41	1.48	1	2	4	4	7

Note. How often have you attended the following cultural venues over the past 12 months? (n = 965): 1 = never, 2 = once a year, 3 = every 6 months, 4 = every few months, 5 = once a month, 6 = several times a month, and 7 = weekly or more often.

Framework

We investigate the effect of charitable giving upon the binary decision of whether a respondent is willing to volunteer using the following (probit) model:

P (V o l_{i} = 1 | X) = φ (α + β_{1} W T P_{i} + β_{2} V a l u e_{i} + β_{3} C o n t r o l s + ε_{i})

(1)

where $V o l_{i}$ is a binary variable indicating whether respondent i indicated they would volunteer to support the provision of the community arts program, $W T P_{i}$ is a numerical variable indicating the dollar amount that participants are willing to pay to prevent a shortfall in arts activity from occurring, $V a l u e_{i}$ is the self-reported measure of the intangible value of arts activity,³ $C o n t r o l s$ is a vector of seven demographic variables, $ε_{i}$ is the error term, and $φ$ represents the standard normal cumulative distribution.

A potential problem is that WTP could be endogenously determined; for example, an individual who volunteers is also more likely to donate (see Apinunmahakul et al., 2007; Bekkers, 2010). There is also likely to be a presence of uncontrolled and difficult to measure confounding variables such as altruism, further driving concerns of endogeneity.

We address this concern using a two-stage least squares (2SLS) estimation technique, employing a previously unused instrument in the form of engagement with arts activities unrelated to those focused upon in the arts programs under consideration. The instrument measures the 12-month overall frequency of arts engagement and attendances to cultural venues as shown in Table 3. The implicit assumption is that participation in arts activities, in general, is correlated with WTP for similar activities but has no direct effect on the propensity to volunteer for the particular organization targeted in this study. We also instrument with postcode-level measures of the Index of Relative Socio-Economic Disadvantage (ABS, 2001), expecting it to be strongly correlated with each participant’s WTP. Accordingly, relevance and exclusion restrictions are evaluated.

Discussion of Findings

We find common characteristics in our potential volunteers. They are more likely to be females, from the younger age group, who speak a second language, at the lower and upper income levels, and higher levels of education levels. The strongest association is with those who are willing to pay are also more likely to be willing to volunteer. We also find strong associations with nonuse cultural attitudes associated with willingness to donate time and money. These attitudes include perceptions about the role culture and the arts play in developing a national and international profile, the contribution of arts and culture to innovation in the economy, and that the arts and culture should be preserved for future generations to enjoy. Prosumption appears to play a part as well, with an individual’s cultural consumption able to predict who is more likely to be a potential volunteer in community arts programs.

We focus our discussion on the IV version of our model (Table 4; Model 1) which is a binary IV-probit of volunteering with WTP and a set of controls as independent variables, and where WTP is instrumented using arts engagement and socioeconomic disadvantage. An ordinary probit (Model 2) with the same variable specification is also presented. As anticipated, WTP is endogenous (Table 5; Wald Test), justifying the use of 2SLS—albeit that the final results of both models are very similar. Importantly, our IV diagnostics in Table 5 also show that this IV augmentation is appropriate, namely, the instruments are linearly associated with WTP but not the error term and, furthermore, they are not weak.

Table 4.

Volunteer Probit and IV Probit Model Results.

Variables		1	2
Variables		IV probit(WTP)	Probit
Age	25–34 years	0.0682 (0.233)	−0.181 (0.196)
	35–44 years	−0.104 (0.234)	−0.352* (0.198)
	45–54 years	−0.253 (0.237)	−0.491** (0.202)
	55–64 years	−0.577** (0.249)	−0.770*** (0.218)
	65 years and above	−0.667** (0.307)	−0.912*** (0.269)
LOTE	Yes	0.332*** (0.127)	0.398*** (0.112)
Annual household income	AU$15,000–AU$30,000	−0.525* (0.311)	−0.339 (0.270)
	AU$31,000–AU$60,000	−0.344 (0.294)	−0.256 (0.259)
	AU$61,000–AU$100,000	−0.451 (0.292)	−0.340 (0.256)
	AU$101,000–AU$150,000	−0.519* (0.312)	−0.370 (0.273)
	AU$151,000–AU$200,000	−0.950*** (0.356)	−0.557* (0.296)
	AU$201,000–AU$250,000	−0.508 (0.431)	−0.438 (0.384)
	Above AU$250,000	−0.280 (0.462)	−0.143 (0.409)
	Prefer not to answer	−0.517* (0.298)	−0.480* (0.264)
Employment	Unwaged (e.g., home duties)	0.0918 (0.237)	0.0732 (0.213)
	Studying full-time	0.203 (0.242)	0.0459 (0.212)
	Not currently working	0.0959 (0.187)	0.00475 (0.167)
	Retired	0.195 (0.228)	0.0803 (0.205)
Education	Certificate or diploma	0.232 (0.145)	0.281** (0.131)
Education	Higher education	0.365*** (0.141)	0.454*** (0.125)
Gender	Female	0.268** (0.120)	0.113 (0.0988)
Distance from target municipality (km)		−3.93e-05 (0.00130)	0.000241 (0.001)
Nonuse value factor		0.224*** (0.0617)	0.294*** (0.052)
Willingness to pay (AU$)		0.0153*** (0.00320)	0.006*** (0.0009)
Constant		−0.823** (0.359)	−0.415(0.300)
Observations		948	948
Pseudo R²		N/A	.1661
Wald/LR χ²(24)		118.68	118.46
Prob > χ²		0.0000	0.0000

Note. All prices are expressed in current Australian Dollar (AUD) terms. Standard errors are in parentheses. WTP = willingness to pay; LOTE = language other than English.

p < .10. **p < .05. ***p < .01.

Table 5.

Instrumental Variable Diagnostics for Model 1.

Test	Statistic	p value
Wald test of exogeneity	11.07	.0009
Sargan–Hansen test of overidentifying restrictions	0.001	.9992
Anderson canonical correlation underidentification test	64.38	.0001
Cragg–Donald Wald F statistic of weak identification	33.588	N/A

As shown in Table 4, we find that speaking a language other than English (LOTE) is positively and significantly associated with the probability of volunteering. This result is consistent with the findings by Wilson (2012). We also find that gender is a significant factor, with females substantially more likely to volunteer. For “household income,” at the 10% level, the AU$15,000–AU$30,000, AU$101,000–AU$150,000, and AU$151,000–AU$200,000 income brackets were significantly different from the base case of less than AU$15,000 (β = –.525, p = .092; β = –.519, p = .096; and β = –.949, p = .008, respectively). In contrast to Apinunmahakul et al. (2007), we find that employment status does not have a statistically significant association with the WTV. We also find that age has a significant negative impact on the decision to volunteer (p < .05) for ages 55 years and above, as compared with the younger age groups.

Our findings on the decision to volunteer are consistent with Bekkers (2010), Bussell and Forbes (2002), and Gray et al. (2012) in that volunteers are more likely to be younger and female. The marginal probabilities of volunteering at mean levels across age categories are graphed in Figure 1 and show a gradual decline in the probability to volunteer as age increases. We find that those who are aged 55 years and above are half as likely to volunteer their time compared with those in the base age category of 18 to 24 years, whereas those who are aged 65 and above are more than 4 times likely to volunteer across all WTP categories.

Figure 1.

Marginal propensity to volunteer at mean level across age categories.

In Table 4, we also reveal that nonuse cultural attitudes are a significant predictor of the decision to volunteer (β = .223, p < .01). Analysis of the responses indicated that there is a high level of association between the different elements. This observation is consistent with Warren (2014), indicating that those who support the arts have a desire to contribute to the community’s shared creative identity.

Our results show that WTP is also positively related to volunteering. The marginal probabilities of volunteering at mean levels across WTP categories are presented in Figure 2 and indicate that there is a steady increase across WTP categories.

Figure 2.

Marginal propensity to volunteer at mean level across WTP categories.

The trajectory mapped in Figure 3 shows there is a gradual increase in WTV as education levels increase. The effect of education is only significant, however, at the “higher education” level (β = .365, p = .010), with these individuals being more than 1.5 times more likely to volunteer compared with those who have only a high school education.

Figure 3.

Marginal propensity to volunteer at mean level across education categories.

This research offers new insight into the role that cultural engagement and attitudes have on the decision to volunteer within the community arts sector. Importantly, we note there are some limitations to our study. First, the data are from a cross-sectional survey representing a point in time and thus issues with common method bias are inherent (Conway & Lance, 2010). Second, although care was taken to construct a meaningful and correctly administered hypothetical scenario, contingent valuation has been subject to criticism due to bias estimates (Armbrecht, 2014; Diamond & Hausman, 1994; Epstein, 2003). Third, the survey did not explore the underlying motivations and personality traits of the participants who were willing to donate time and money. Despite these limitations, we believe that our study shows that Klamer’s (2016) valuation framework based on willingness to contribute is an insightful way to explore the potential volunteer pools for local arts groups.

Conclusion

Beyond the extensive research that has focused on actual volunteers (Burani & Palestini, 2016; Bussell & Forbes, 2002; Dolnicar & Randle, 2007; Gross & Rottler, 2019) and non-volunteers (Haski-Leventhal et al., 2018), we investigate potential volunteers who are not actively engaged in local arts-related programs. We believe that this study provides useful insights to help plan and execute arts events and programs reliant upon volunteer support by revealing some key traits of this important resource.

Specifically, we use the concept of willingness to contribute (Klamer, 2016), which accounts for both WTP and WTV in assessing how much the general public may contribute to support local arts programs. By incorporating questions about willingness to donate time as well as money, we provide evidence to show how these forms of support complement each other, with nuances observed in differences across demographic characteristics, including age, gender, and education.

Furthermore, we find that non-use cultural attitudes highly positively correlate with WTV. We also introduce two potential alternative instruments to deal with the usual issues of endogeneity bias for such models of volunteering, by using a composite measure of “other” cultural attendances and engagement along with socioeconomic status.

Klamer’s (2016) willingness to contribute provided the theoretical underpinning of this study. It enabled the means to explore the value that potential volunteers place on community art programs. We believe the profiling of these potential volunteers can help strengthen and grow local arts organizations in providing their community benefits.

Footnotes

Acknowledgements

The authors would like to thank the City of Melbourne for their contribution and assistance in this investigation.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Meg Elkins

Mehmet Özmen

Notes

Author Biographies

Meg Elkins is a senior lecturer in the School of Economics, Finance, and Marketing at RMIT University in Melbourne, Australia. She uses applied and behavioral techniques that feed into an overarching theme of well-being creation. Her work investigates creative culture for community development in cultural economics and policy evaluation in development economics.

Bronwyn Coate is a senior lecturer in the School of Economics, Finance, and Marketing at RMIT University in Melbourne, Australia. Her research involves economic analysis of the arts and creative industries, using a range of economic and econometric techniques including approaches from behavioral economics.

Mehmet Özmen is an applied econometrician and data scientist interested in applying novel data to economic and social policy questions. He is presently a lecturer in the Department of Economics at the University of Melbourne.

Ashton de Silva is an applied econometrician with a focus on place and regions. His work transcends several areas of economics, including cultural industries, household economics, and well-being, as well as the more traditional domains of economic and financial modeling. He has established a strong track record in industry and academic research.

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