Five shades of green: Uncovering consumption profile heterogeneity and the role of socioeconomic status in the Netherlands

Five shades of green: Uncovering consumption profile heterogeneity and the role of socioeconomic status in the Netherlands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Five shades of green: Uncovering consumption profile heterogeneity and the role of socioeconomic status in the Netherlands Joyce Ying-Ching Pang, Paulina Pankowska, Félice Nunspeet, Vincent Buskens This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7101420/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Discover Sustainability → Version 1 posted 12 You are reading this latest preprint version Abstract Consumer behaviour significantly contributes to environmental degradation, highlighting the need to understand consumption patterns to inform sustainable policies. Despite growing interest, there is still a lack of nuanced understanding of consumption. First, most studies examined how factors like socioeconomic status (SES) influence isolated consumption behaviours, overlooking the cumulative, cross-domain influence of consumption. Second, while SES is a well-acknowledged and studied predictor of consumption, less is known whether the effects of SES components (income, education) differ and whether they interact. To overcome these gaps, we used microdata from Statistics Netherlands and grouped households based on their fashion, food, transportation, water, and energy expenditures via Latent Profile Analysis. Five subgroups are identified: Low consumers, high consumers, nomadic compensators, local-based foodies, and average consumers . Higher-educated households are more likely to be “nomadic compensators” and “average consumers” and less likely to be “local-based foodies”. Higher-income households are less likely to be “local-based foodies”. For other profiles, income and education effects are interdependent. Lower-income or lower-educated households are more likely to be “low consumers”, whereas higher-income or higher-educated households are more likely to be “high consumers”. Among households with lower education levels, higher income increases the probability of belonging to “high consumers”, while lower income increases the probability of belonging to “low consumers”. However, at high-income levels, these differences disappear. Our findings highlight the importance of a person-centered framework in understanding consumption. Consumption Latent Profile Analysis Socioeconomic Status Sustainability Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Mounting evidence has demonstrated that our current consumption patterns are surpassing the carrying capacity of the earth. For instance, world meat consumption has quadrupled since 1961 in absolute and per capita terms (1). Similarly, the average person buys 60% more clothes and keeps them for about half the time they did 15 years ago (2). Consequently, governments and organisations increasingly acknowledge the need to transform our existing consumption patterns to a more sustainable mechanism that induces people to consume less. However, before interception, it is important for us to first understand people’s consumption. Although research is abundant in understanding consumption, most, if not all, studies adopt a variable-centered approach, which examines how specific factors, such as individual characteristics, shape consumption behaviours. For example, research has shown that socioeconomic status, an individual’s rank in society in terms of wealth, occupational prestige, and education (3), correlates positively with energy consumption (4-5). While this substantial line of research has significantly advanced our knowledge of the nature and determinants of different consumption behaviours, the variable-centered approach focused too much on consumption as variables and not enough attention has been given to the consumers who consume. Consequently, the variable-centered approach, which typically relies mostly on correlation and regression techniques, can say little about how multiple consumption behaviours are combined within consumers and how consumers with similar consumption profiles are represented in the population. While we acknowledge that there is a developing body of work in consumption research that adopts a person-centered approach, most, if not all, focus on a single consumption domain such as transportation (6) or food (7). To our knowledge, none has adopted the choice of a person-centered approach in simultaneously examining a comprehensive set of consumption domains. Thus, the study aims to address this question. Additionally, we aim to advance our understanding of how SES influences consumption as contemporary studies mostly examined the influence of SES components in isolation or the influence of an aggregate SES index without examining whether the effects of SES components differ and whether the effect of one SES component [1] (e.g., income) on consumption is contingent upon another component (e.g., education). Ample evidence demonstrates that the two components correspond to different resources. For instance, whereas education is a better index for cultural resources, income is a more direct index for material resources and purchasing power (8). Yet, little is known about how the two components differ in their effects on consumption and whether they interact to influence consumption jointly. We analysed the difference in income and education effects on various consumption domains. Furthermore, we explored how the impact of income on some types of consumption can be contingent upon people’s education level or, conversely, how the impact of education on consumption is contingent upon people’s income level. Our study adopted a latent profile analysis (LPA), which is a multidimensional approach that allows us to classify households into meaningful, relatively homogenous groups according to their consumption. Furthermore, we observed whether SES, namely income and education, was associated with the identified profiles. We focused on SES because it is consistently associated with different types of consumption (4-5,9). We aim to advance the literature on consumption in two ways. First, to our knowledge, this is the first study that applies LPA to the study of consumption. By moving beyond the variable-centred approaches, we offer a nuanced, multidimensional understanding of consumption. Second, we deepen the nuanced understanding by examining how SES components not only differ in their effects on consumption but also interact to affect consumption domains differently. By providing a comprehensive understanding of consumption, the findings of this study offer crucial insights in evaluating consumption patterns of different groups in terms of their sustainability. The comprehensive evaluation can then guide the development of integrated interventions in promoting socially responsible consumption that balance multiple sustainability goals and account for the diverse responses across income and education groups. 1.1 From a Variable-Centered to a Person-Centered Approach Currently, the literature on consumption has adopted a variable-centered approach and mainly examines different domains of consumption separately. That is, many studies on consumption to date are limited to one or two specific dimensions of consumption in combination with one explanatory factor. For instance, consumption research focuses on the relationship between household income and water usage (10) and between education and clothes shopping (11). Even though these studies advance our knowledge of the antecedents within individual consumption domains, their predominant focus on isolated consumption domains has ignored the inherently interconnected and multidimensional nature of consumption. In real-life contexts, consumers make a series of decisions in which one choice follows another. Indeed, the spillover literature from environmental psychology corroborates the notion that engaging in one behaviour affects the probability of engaging or disengaging in a second behaviour (12). For example, individuals engage in otherwise environmentally harmful behaviours after having engaged in prior environmentally friendly actions ( negative spillover ). Alternatively, individuals may engage in more environmentally friendly behaviours after performing an environmentally friendly action ( positive spillover ). While several scholars have acknowledged the multidimensionality of consumption (13–16), most existing research still limits its scope to one or two domains, seldom addressing multiple consumption areas simultaneously. Another reason to examine multiple consumption domains simultaneously is that the influence of consumption predictors may vary across domains. For instance, not all consumption behaviours are subjected to normative influence to a similar extent, partly because they differ in how visible they are to others. Individuals may know which of their neighbours bike to work, but they may not know which neighbours are taking longer showers. Following this logic, it is plausible that visible consumption (e.g., clothes purchase) is more likely to be subjected to normative influence when compared to less visible consumption (e.g., water usage), even for people who genuinely have a pro-environmental stance as they can then signal their stance as an environmentalist to others (that is, the so-called “green to be seen” phenomenon) (17). Notably, visibility may not always lead to reduced consumption as people having a counter-environmental stance may consider pro-environmental behaviours as signalling an unwanted social identity of “environmentalist”, in turn, leading to more consumption (that is, the so-called “brown to keep down” phenomenon) (17). To that end, we ought to consider consumption behaviours of varying levels of visibility to paint a comprehensive consumption profile. In our study, for instance, meat consumption is a visible form of consumption whereas water usage is a less visible one. Above all, examining isolated consumption behaviours may also lead to an inaccurate estimation of people’s consumption on the extent to which one is environmentally friendly. The misrepresentation can hinder efforts to promote sustainable consumption, as it may lead to targeting the wrong groups or domains with interventions. Despite the potential of a person centered-approach in consumption, empirical evidence is still lacking in this area. Therefore, the first aim of the study is to build consumption profiles using a multiplicity of consumption behaviours concerning fashion, food, transport, water, and energy, as they are the primary sources of individual carbon contribution (18). Since this is, to our knowledge, the first study that applies LPA on multiple consumption domainsvq, the type and number of profiles that would emerge in the analysis is unknown. To that end, no hypotheses regarding the profiles are made. 1.2 SES and Consumption The second aim of the study is to delineate the role of SES in consumption. SES is typically measured by income, educational attainment, or their combinations (19). For instance, scholars have documented a positive correlation between income and energy consumption (4-5) and a negative correlation between education and meat consumption (9,20). While research on the effects of separate SES components or on an aggregate SES index on consumption is abundant, comparatively little is known about their isolated effects and their interplay, that is, whether the effects of different components differ on having a certain consumption profile and whether the effect of one SES component (e.g., income) on consumptions is contingent upon another components (e.g., education). However, there is good conceptual reason to consider the effects and interplay of education and income level as they tap into different resources and opportunities. Drawing from Bourdieu’s capital theory (21), income and education correspond to two different resources, which, in turn, shape consumption in different ways. Income reflects economic capital, a more direct index for material resources and purchasing power, whereas education reflects cultural capital, a better index for cultural resources (8,21). Since sustainability issues are usually grounded in the language of scientific knowledge (22), higher-educated individuals may value such messages to a greater extent than people with a lower education level, as higher-educated individuals frequently cite and rely on expert knowledge in their tertiary education and are also taught to value such expert knowledge over other sources of knowledge such as tradition, kin, or peer groups (23). As a result, higher-educated individuals may be more aware of the potential environmental consequences of their actions and more likely to acknowledge their personal responsibility to be environmentally friendly, in turn, resulting in lower consumption. In addition to the provision of different resources, the social norms associated with environmentalism may vary as a function of income and education level. One common perception people have of environmentalists is that they are highly educated (24). Given the stereotype about environmentalists, higher-educated individuals may view pro-environmental norms as an in-group norm, whereas individuals with a high-income level may not. According to self-categorisation theory (25), normative influence is stronger when the norms are associated with self-relevant groups, in turn, higher-educated individuals may have lower consumption because of the highly self-relevant pro-environmental norms. Using meat consumption as an illustration, one with high income and education may have more financial capability to afford meat products. However, they may still consume relatively less meat because they are more aware of the environmental damage and health consequences brought by (excessive) meat consumption. Conversely, one with high income and low education may consume more meat because they may have less exposure to scientific knowledge about the negative environmental consequences of meat consumption and experience less normative pressure to behave environmentally friendly. Therefore, individuals with high income and low education can afford meat products while facing fewer constraints. Therefore, meat purchases may be linked to financial resources, but possibly less for people with a higher education level. Such a supposition also applies to other consumption domains, such as energy usage. Studies on the relationship between education level and energy-efficient investments yielded inconsistent results. While some studies found no correlation (26-27), some documented a positive correlation (28–30). Though it has yet to be empirically examined, a possible explanation proposed by scholars is that higher educated people tend to have higher income levels, which increases energy use (31-32), but also the probability of purchasing energy-efficient investments (27,31). The explanation suggests that the effect of education on the purchase of energy-efficient investments is contingent upon people’s income level. 1.3 Heterogeneous Effects of SES on Consumption Notably, we do not argue that individuals with high income and education will always result in low consumption across all domains. For instance, empirical studies have consistently found that the probability of travelling by plane increases with education and income level (33–35). A possible explanation would be that individuals of higher education levels have an increased need to participate in culturally enriching activities to satisfy their taste of culture and aesthetic pleasures (21). Thus, they may have increased travel and fashion expenditures despite a high level of environmental concern. Alternatively, drawing from the moral licensing literature in environmental psychology, another possible explanation is that individuals with high income and education feel morally justified to behave in an environmentally unfriendly manner in certain domains because they have had low consumption in other domains (36). For instance, among individuals with a high level of environmental concern, those reminded of their energy-efficient investments reported lower motivation to reduce air travel (37). In fact, we do not argue that the same combination of income and education will always result in homogeneous effects across consumption domains. It is possible that consumption behaviours across different domains are sometimes uncorrelated. On the other hand, when they are correlated, consumption behaviour in one domain influences consumption behaviour in another, either negatively ( negative spillover ) or positively ( positive spillover ). For instance, researchers found a negative relationship between household water use and electricity consumption (38). Nonetheless, scholars also documented a positive relationship between fuel-efficient driving styles and intentions to reduce meat consumption (39). While there has been research on the role of identity and situational factors in increasing or decreasing the likelihood of positive and negative spillover effects (29,40), it remains unknown how people’s SES may influence the directionality of spillover. There are hints that environmental concern, which relates to people’s education level, plays a role in determining the directionality, yet results have been inconsistent (29,37,40–42). Altogether, the discussion suggests that there may be notable differences between income and education in predicting consumption and that the two may interact to influence consumption jointly. Given the compiled arguments and evidence, no straightforward predictions are possible regarding the direction of the relationships between SES components and consumption behaviours. Therefore, this study is an exploratory study as we cannot predict which profiles would emerge and how SES would influence the probability of belonging to any identified profiles. Hence, no hypotheses were proposed, and we posed the following research questions. RQ1. What consumption profiles exist in the population of the Netherlands concerning consumption behaviours related to fashion, food, transportation, water, and energy? RQ2. How does the probability of belonging to any consumption profile depend on people’s income and education level? [1]Although many more SES components can be distinguished (e.g., cultural capital, wealth, occupation), income and education are two of the most commonly used metrics of SES and are the most comprehensively available in the data we utilised in the present study. Therefore, we focused on the role of income and education in this paper. 2. Methodology 2.1 Data To answer RQ1, we used survey data from the Dutch Centraal Bureau voor de Statistiek (CBS) to identify consumption patterns in the Dutch population. We mainly utilised the expenditure survey (budgetonderzoek), in which participating households record all monthly expenses exceeding €20 and weekly expenses under €20. At year’s end, they report significant expenses, such as a new car or vacations. The survey is conducted every five years, and we used the expenditure survey from the 2015 cohort ( N = 14,404) as it represents the most recent version of pre-COVID data. To understand how income and education affect the probability of belonging to identified consumption profiles (RQ2), we matched participating households in the expenditure survey with their corresponding register data on the education level through a unique anonymised identifier. Register data on education (hoogsteopltab), provided by Dutch education authorities, contains the highest attained education level of the individuals. Household income is an existing variable in the expenditure survey. Additionally, we included household ownership, the degree of urbanisation to which a household belongs, and household composition as control variables when estimating the relationship between SES and consumption profiles. 2.2 Consumption Behaviours Consumption behaviours were measured using eleven indicators (Table 1 ). Since we would likely overestimate the consumption of larger households as they inevitably have more expenditures, we recalculated a per capita household expenditure by dividing the total household expenditure by the number of people living in the household. Table 1 Ten Indicators that Covered Five Targeted Consumption Domains Types of consumption Per capita household expenditure on Food ( 1 ) Meat ( 2 ) Fish Table 1 (Continued.) Types of consumption Per capita household expenditure on ( 3 ) Dairy products ( 4 ) Vegetables Travel ( 5 ) Flights ( 6 ) A constructed variable for expenditures on private transport, which is a sum of expenditures on fuels (including petrol, diesel, and other fuels) and taxis. ( 7 ) A constructed variable for expenditures on public transport, which is a sum of expenditures on trains and buses Fashion ( 8 ) Clothes and shoes Energy ( 9 ) Electricity ( 10 ) Gas Water ( 11 ) Water usage The selection of indicators was based on several considerations. Namely, the selected indicators were diverse and represented the five consumption domains targeted in our study. Further, as indicators with high response variability are more useful in distinguishing different consumption patterns, indicators with (very) low variance were excluded from the analysis. 2.3 Socioeconomic Status (SES) 2.3.1 Household Income Taken from the expenditure survey, we operationalised household income as the mean-centered annual disposable income of a household, which was treated as a continuous variable in our study. A household’s disposable income is the gross household income minus any income transfers such as health insurance and income taxes. 2.3.2 Education Research has shown that taking the highest education level among parents to represent the family does not provide an accurate influence of education on household outcomes ( 44 ). Therefore, based on the register data on education, we operationalised the education level of a household by obtaining the mode of the education level of people who are above 25 years old in a household, as they are more likely to complete their education and are conventionally the ones who make decisions for the household. Such approach is also more commonly used in operationalising household SES in family sociology ( 45 ). Using the international standard classification of education as the basis , we distinguished between four levels of education: low, medium, high, and very high. The low education level includes groups 1 through 8 (all years) of primary and special primary education plus the first three years of senior general secondary education (HAVO) and pre-university secondary education (VWO); the various pathways of prevocational secondary education (VMBO) including lower secondary vocational training and assistant’s training (MBO-1). The medium education level includes upper secondary education (HAVO/VWO), basic vocational training (MBO-2), vocational training (MBO-3), and middle management and specialist education (MBO-4). The high education level refers to associate degree programmes, bachelor’s programs, and master’s programs at Universities of Applied Sciences (HBO). The very high education level refers to bachelor’s, master’s, and doctoral programs at research universities (WO). 2.4 Control Variables Additionally, we controlled for three variables, namely household ownership, household composition, and the degree of urbanisation, as literature has shown that they are related to consumption. Theoretical justifications for each control variable can be found in Appendix A. 2.4.1 Household Ownership Taken from the expenditure survey, household ownership originally had three levels: rented, rented with rental allowance, and own property. For the sake of simplicity, we recoded the variable so that it only distinguishes between rented vs. owned property in our analysis. 2.4.2 Household Composition Taken from the register database (Gbapersoontab), which contains microdata on the personal characteristics of individuals registered in the Personal Records Database (BasisRegistratie Personen; BRP), we coded the total number of children (0–12 years old), teenagers (13–18 years old), adults ( 19 – 66 ), and elderlies (> 67) in a household. 2.4.3 The Degree of Urbanisation Taken from the expenditure survey, we utilised an existing categorical variable that measures the concentration of human activities based on the average environmental address density. The surrounding address density was classified into five categories: not urbanised, with fewer than 500 addresses per square kilometre; hardly urbanised, with 500 to 1,000 addresses per square kilometre; moderately urbanised, with 1,000 to 1,500 addresses per square kilometre; strongly urbanised with 1,500 to 2,500 addresses per square kilometre; and extremely urbanised, with 2,500 addresses or more per square kilometre. 2.5 Data cleaning To handle extreme values in the income variable and expenditures, we identified outliers using the Tukey’s method ( 46 ), which defines outliers as observations that exceed 1.5 times the interquartile range (IQR) beyond the first quartile (Q1) or above the third quartile (Q3). We winsorised the outliers by replacing values below this threshold with Q1 and values above this threshold with Q3. This approach allows us to reduce the impact of extreme values while preserving the overall distribution of the data. Furthermore, all negative expenditures, which likely indicate recovered money, such as goods sold and returns (e.g., a negative meat expenditure could mean that the households earn money by selling meat), were coded to 0. The recode ensured our analysis remains solely focused on understanding household consumption behaviours without interference from values that may represent income or productive activities. The occurrence of negative expenditures was rare, accounting less than 2% across all indicators. All data preparation and cleaning were performed using the R statistical computing software 4.4.0 ( 47 ). 2.6 Data Analysis 2.6.1 RQ1 Consumption Patterns The first step of the analysis focused on identifying different consumption patterns among Dutch households in 2015 using latent profile analysis (LPA). The analysis was conducted in Latent Gold version 6.0 ( 48 ). LPA is used to identify latent unobserved variables that explain interrelations between a set of observed indicators. Put differently, LPA uncovers hidden groupings or sub-populations based on observed data ( 49 ). It has a wide range of applications, including identifying political preferences ( 50 ), understanding different personality types or traits ( 51 ), and discovering consumption patterns in isolated domains ( 52 ). As the number of profiles was not known a priori, several models were estimated with the number of profiles ranging from one to ten. The final model was then selected based on two sets of criteria, including statistical (i.e., the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC)) and substantive considerations, i.e., interpretability, theoretical relevance, and profile size ( 49 ). 2.6.2 RQ2: The Effect of SES on Consumption Profiles The second part of the analysis tested how SES components (independent variables) affected the probability of belonging to the resultant consumption profiles. In our analysis, we used a bias-adjusted three-step approach ( 48 , 53 ). In the first step, we used a latent profile model that can formalised as, $$\:P\left(Y=y\right)=\:\sum\:_{x=1}^{k}P\left(X=x\right)P\left(Y=y\:\right|X=x)$$ Here, \(\:P(Y=y)\) denotes the probability of observing a specific pattern of consumption across the ten indicators considered. \(\:P(X=x)\) then indicates the probability of belonging to a specific latent profile \(\:x\) , and \(\:k\) indicates the number of profiles. Finally, \(\:P\left(Y=y|X=x\right)\) indicates the probability of observing a specific pattern of responses \(\:y\) given profile membership \(\:x\) . In the second step, the estimated individual-level profile membership was saved in the data, and in the third step, we used a multinomial logistic regression model to estimate how income, education, and their interaction (independent variables) predict the probability of belonging to the obtained profiles (dependent variables), while considering other control variables. In doing so, we used a correction to account for the uncertainty of class membership ( 54 ). The model used in the analysis can also be graphically explained as follows (Fig. 1 ) 3. Results 2.1 Data To answer RQ1, we used survey data from the Dutch Centraal Bureau voor de Statistiek (CBS) to identify consumption patterns in the Dutch population. We mainly utilised the expenditure survey (budgetonderzoek), in which participating households record all monthly expenses exceeding €20 and weekly expenses under €20. At year’s end, they report significant expenses, such as a new car or vacations. The survey is conducted every five years, and we used the expenditure survey from the 2015 cohort ( N = 14,404) as it represents the most recent version of pre-COVID data. To understand how income and education affect the probability of belonging to identified consumption profiles (RQ2), we matched participating households in the expenditure survey with their corresponding register data on the education level through a unique anonymised identifier. Register data on education (hoogsteopltab), provided by Dutch education authorities, contains the highest attained education level of the individuals. Household income is an existing variable in the expenditure survey. Additionally, we included household ownership, the degree of urbanisation to which a household belongs, and household composition as control variables when estimating the relationship between SES and consumption profiles. 2.2 Consumption Behaviours Consumption behaviours were measured using eleven indicators (Table 1). Since we would likely overestimate the consumption of larger households as they inevitably have more expenditures, we recalculated a per capita household expenditure by dividing the total household expenditure by the number of people living in the household. Table 1 Ten Indicators that Covered Five Targeted Consumption Domains Types of consumption Per capita household expenditure on Food (1) Meat (2) Fish Table 1 (Continued.) Types of consumption Per capita household expenditure on (3) Dairy products (4) Vegetables Travel (5) Flights (6) A constructed variable for expenditures on private transport, which is a sum of expenditures on fuels (including petrol, diesel, and other fuels) and taxis. (7) A constructed variable for expenditures on public transport, which is a sum of expenditures on trains and buses[2] Fashion (8) Clothes and shoes Energy (9) Electricity (10) Gas Water (11) Water usage The selection of indicators was based on several considerations. Namely, the selected indicators were diverse and represented the five consumption domains targeted in our study. Further, as indicators with high response variability are more useful in distinguishing different consumption patterns, indicators with (very) low variance were excluded from the analysis. 2.3 Socioeconomic Status (SES) 2.3.1 Household Income Taken from the expenditure survey, we operationalised household income as the mean-centered annual disposable income of a household, which was treated as a continuous variable in our study. A household’s disposable income is the gross household income minus any income transfers such as health insurance and income taxes. 2.3.2 Education Research has shown that taking the highest education level among parents to represent the family does not provide an accurate influence of education on household outcomes (44). Therefore, based on the register data on education, we operationalised the education level of a household by obtaining the mode of the education level of people who are above 25 years old in a household, as they are more likely to complete their education and are conventionally the ones who make decisions for the household. Such approach is also more commonly used in operationalising household SES in family sociology (45). Using the international standard classification of education as the basis[3], we distinguished between four levels of education: low, medium, high, and very high. The low education level includes groups 1 through 8 (all years) of primary and special primary education plus the first three years of senior general secondary education (HAVO) and pre-university secondary education (VWO); the various pathways of prevocational secondary education (VMBO) including lower secondary vocational training and assistant’s training (MBO-1). The medium education level includes upper secondary education (HAVO/VWO), basic vocational training (MBO-2), vocational training (MBO-3), and middle management and specialist education (MBO-4). The high education level refers to associate degree programmes, bachelor’s programs, and master’s programs at Universities of Applied Sciences (HBO). The very high education level refers to bachelor’s, master’s, and doctoral programs at research universities (WO). 2.4 Control Variables Additionally, we controlled for three variables, namely household ownership, household composition, and the degree of urbanisation, as literature has shown that they are related to consumption. Theoretical justifications for each control variable can be found in Appendix A. 2.4.1 Household Ownership Taken from the expenditure survey, household ownership originally had three levels: rented, rented with rental allowance, and own property. For the sake of simplicity, we recoded the variable so that it only distinguishes between rented vs. owned property in our analysis. 2.4.2 Household Composition Taken from the register database (Gbapersoontab), which contains microdata on the personal characteristics of individuals registered in the Personal Records Database (BasisRegistratie Personen; BRP), we coded the total number of children (0-12 years old), teenagers (13-18 years old), adults (19-66), and elderlies (>67) in a household. 2.4.3 The Degree of Urbanisation Taken from the expenditure survey, we utilised an existing categorical variable that measures the concentration of human activities based on the average environmental address density. The surrounding address density was classified into five categories: not urbanised, with fewer than 500 addresses per square kilometre; hardly urbanised, with 500 to 1,000 addresses per square kilometre; moderately urbanised, with 1,000 to 1,500 addresses per square kilometre; strongly urbanised with 1,500 to 2,500 addresses per square kilometre; and extremely urbanised, with 2,500 addresses or more per square kilometre. 2.5 Data cleaning To handle extreme values in the income variable and expenditures, we identified outliers using the Tukey’s method (46), which defines outliers as observations that exceed 1.5 times the interquartile range (IQR) beyond the first quartile (Q1) or above the third quartile (Q3). We winsorised the outliers by replacing values below this threshold with Q1 and values above this threshold with Q3. This approach allows us to reduce the impact of extreme values while preserving the overall distribution of the data. Furthermore, all negative expenditures, which likely indicate recovered money, such as goods sold and returns (e.g., a negative meat expenditure could mean that the households earn money by selling meat), were coded to 0. The recode ensured our analysis remains solely focused on understanding household consumption behaviours without interference from values that may represent income or productive activities. The occurrence of negative expenditures was rare, accounting less than 2% across all indicators. All data preparation and cleaning were performed using the R statistical computing software 4.4.0 (47). 2.6 Data Analysis 2.6.1 RQ1 Consumption Patterns The first step of the analysis focused on identifying different consumption patterns among Dutch households in 2015 using latent profile analysis (LPA). The analysis was conducted in Latent Gold version 6.0 (48). LPA is used to identify latent unobserved variables that explain interrelations between a set of observed indicators. Put differently, LPA uncovers hidden groupings or sub-populations based on observed data (49). It has a wide range of applications, including identifying political preferences (50), understanding different personality types or traits (51), and discovering consumption patterns in isolated domains (52). As the number of profiles was not known a priori, several models were estimated with the number of profiles ranging from one to ten. The final model was then selected based on two sets of criteria, including statistical (i.e., the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC)) and substantive considerations, i.e., interpretability, theoretical relevance, and profile size (49). 2.6.2 RQ2: The Effect of SES on Consumption Profiles The second part of the analysis tested how SES components (independent variables) affected the probability of belonging to the resultant consumption profiles. In our analysis, we used a bias-adjusted three-step approach (48,53). In the first step, we used a latent profile model that can formalised as, 3.1 Descriptive Statistics Table 2 provides an overview of the means and standard deviations of the eleven consumption indicators used in the LPA, as well as the proportions of the main independent variables that influence profile membership. Information regarding the control variables in the model can be found in Appendix B. A correlation matrix of the eleven indicators can be found in Appendix C. Table 2 Summary Statistics for the Main Variables in the Analysis Indicators Mean Standard Deviation Meat 261.73 165.53 Fish 35.33 44.76 Dairy 172.59 106.15 Vegetables 201.83 113.95 Fashion 555.58 484.18 Table 2 (Continued.) Indicators Mean Standard Deviation Flights 53.16 70.04 Private transportation 572.80 376.74 Public transportation 7.45 9.71 Electricity 259.90 99.85 Gas 483.82 189.31 Water 116.52 32.42 Independent variables Income 32226.66 18469.38 Household education Low 5.6% Medium 19.1% High 23.8% Very High 16.7% Missing values[4] 34.8% We can observe high variability in all indicators apart from water expenditures. Further, all indicators correlate with one another significantly, except for the correlation between private and public transportation. 3.2 Identifying Consumption Patterns The next subsection highlights the steps undertaken in the LPA to identify consumption patterns and present the results. 3.2.1 Model Selection Models, with the number of profiles — k —varying from one to ten, were considered. The selection of the best-fitting model was made based on statistical criteria, that is, the model fit expressed using BIC, AIC, and substantive criteria, that is, profile interpretability and size (49). Because of model convergence issues, we adopted a stepwise approach in selecting the best-fitting model. We first focused on the selection of the optimal number of profiles based on the most parsimonious model with respect to the assumptions regarding the variance-covariance matrix (namely, equal variance and zero covariance). Upon selection, we started relaxing the variance assumption for one indicator at a time to determine whether freeing a specific indicator improved the model fit substantially. In the end, we selected the five-profile model with varying variances for three indicators (fish, flights, and public transportation) for further substantive analyses. We chose to retain the zero-covariance assumption. Overall, we had a good quality of classification, with entropy = 0.973. The precise steps in selecting the best-fitting model can be found in Appendix E. 3.3 The Five-Profile Model Figure 2 displays the centered mean scores on the eleven indicators. All indicators were standardised to a mean of 0 and a standard deviation of 1 to facilitate comparability and interpretation of the profiles[5]. The first profile, which included 27% of the sample, was characterised by an overall low expenditure as the means of all indicators were lower in this profile than in others. This profile was accordingly labelled as “ low consumers ”. Conversely, the second profile, which included 12% of the sample, was characterised by an overall high expenditure across all indicators. We termed the second profile “ high consumers ”. The third profile, which amounted to 31% of the sample, was the largest of all five profiles and shared a high expenditure on flights and public transportation but a low expenditure on all food-related indicators. The third profile demonstrated a dissonant consumption pattern, which may appear paradoxical. While households in the third profile spent less on environmentally costly food (e.g., dairy products and meat)— suggesting environmentally friendly behaviours— they simultaneously had high expenditures on flights, a notably environmentally harmful costly activity. The contrast led to the profile being labelled “nomadic compensators”, capturing the dissonance between their environmentally friendly food preferences and carbon-intensive travel behaviours. In contrast, the fourth profile, comprising about 18% of the sample, displayed an opposing yet similarly dissonant pattern. Households in this profile spent relatively more on food, including the environmentally costly categories like dairy and meat, while keeping travel expenses low. It appears that these households tend to spend more locally and focus on food, therefore, this profile was labelled “local-based foodies”. The final profile included about 12% of the sample. Households in this profile exhibited consumption patterns that closely aligned with the average across most domains apart from residential utilities, which was low electricity, gas, and water consumption when compared to other profiles. Because the expenditures for most indicators hovered around the mean, we termed this profile “ average consumers ”. 3.3.1 Descriptives per Profile Across the profiles, “high consumers” had the highest income, followed by “average consumers”, “nomadic compensators”, “local-based foodies”, and “low consumers”. When examining education levels, “average consumers” had the highest proportion of households with a very high level of education, followed by “nomadic compensators”, “high consumers”, “local-based foodies” and “low consumers”. Refer to Appendix G for the detailed figures. 3.4 The Probability of Belonging to Different Profiles In the final part of the analysis, we used multinomial logistic regression to predict the probability of belonging to the identified profiles as a function of education, income, and control variables. Effect coding was used in the analyses, and we assessed whether a given characteristic increases or decreases the likelihood of belonging to a particular profile relative to the average likelihood of profile membership across all profiles. See Table 3 for the statistical significance of the SES and control variables on profile memberships. Table 3 Consumption Profiles: Effects of SES and Control Variables on Profile Memberships Low consumers High consumers Nomadic compensators Local-based foodies Average consumers B z-score B z-score B z-score B z-score B z-score Education -0.230 -9.993* 0.183 5.008* 0.123 5.528* -0.226 -8.512* 0.149 4.463* Income -0.444 -15.257* 0.548 15.427* -0.010 -0.385 -0.127 -4.013* 0.032 0.847 Interaction education income 0.114 3.526* -0.111 -2.561* -0.049 -1.694 0.009 0.254 0.037 0.843 Household ownership 0.002 0.044 0.104 1.613 -0.187 -4.563* 0.037 0.727 0.043 0.619 Degree of urbanisation -0.134 -10.201* 0.094 4.869* 0.054 4.248* -0.091 -6.081* 0.077 4.014* No. of children 0.236 7.268* -1.000 -9.563* 0.042 1.272 0.238 6.723* 0.484 12.638* No. of teenagers -0.016 -0.409 -0.558 -6.750* 0.018 0.489 0.128 3.088* 0.427 9.558* No. of adults -0.113 -4.536* -0.193 -6.031* -0.161 -6.953* 0.037 1.414 0.430 14.114* No. of elderlies -0.198 -5.507* -0.085 -1.933 -0.418 -11.851* 0.216 5.792* 0.486 9.779* Note. The B s represent unstandardised coefficients. * indicates significant z-score in which the z-score falls beyond the range of -1.96 to 1.96, thus, the variable is significant in predicting the probability of belonging to a certain profile. Income was mean-centered for analyses and ranged from -1.87 to 1.77. A negative coefficient indicates a lower probability of belonging to that profile relative to the average probability across all profiles, while a positive coefficient suggests a higher probability. 3.4.1 SES variables Education effects show that households with an overall high level of education were significantly more likely to be in “nomadic compensators” ( B = 0.123, z = 5.528) and “average consumers” ( B = 0.149, z = 4.463), whereas households with an overall high level of education were significantly less likely to be in “local-based foodies” ( B = -0.226, z = -8.512) compared to other types of households. The income effect showed that households with more income were significantly less likely to be in the local-based foodies ( B = -0.127, z = -4.013) compared to other types of households. In the case of “low consumers” and “high consumers”, the effect of income ( B = -0.444, z = -15.257; B = 0.548, z = 15.427, respectively) and education ( B = -0.230, z = -9.993; B = 0.183, z = 5.008, respectively) depended on each other. We observed significant interactions between income and education in predicting the probability of belonging to “low consumers” ( B = 0.114, z = 3.526) and “high consumers” ( B = -0.111, z = -2.561). To provide a better understanding of the interactions, we visualised the interactions in the following graphs. Figure 3 shows how the interaction between education and income influences the probability of belonging to “low consumers”, whereas Figure 4 shows how the interaction between education and income influences the probability of belonging to “high consumers”. Figure 3 shows that households with less income or with a lower education level are more likely to be “low consumers”, whereas Figure 4 indicates households with more income or with a higher education level are more likely to be in “high consumers”. More importantly, the effect of income on the probability of belonging to “low consumers” and “high consumers” was moderated by the education level (see Figures 3 and 4, respectively). While higher income decreased the likelihood of being in a “low consumers” profile and increased the likelihood of being in a “high consumer” profile, this effect was stronger in households with lower education levels. However, the difference in likelihood based on education vanished at high-income levels. At high-income levels, households of all education levels shared a similarly low and high probability of belonging to “low consumers” and “high consumers”, respectively. 3.4.2 Control Variables Given the exploratory nature of the paper, we further explored how several control variables predict the probability of belonging to different profiles. Household Ownership. Households who purchased their home were significantly less likely to be in the nomadic consumer profile ( B = -0.187, z = -4.563) compared to households who rented their home. Degree of Urbanisation. Households living in more urbanised regions were significantly more likely to be in the “high consumers” ( B = 0.094, z = 4.869), “nomadic compensators” ( B = 0.054, z = 4.248), and “average consumers” profiles ( B = 0.077, z = 4.014). Conversely, households living in more urbanised regions were significantly less likely to be in the “low consumers” ( B = -0.134, z = -10.201) and “local-based foodies” profiles ( B = -0.091, z = -6.081). Household Dynamics. Compared to households with fewer children, households with more children were significantly more likely to be in the “low consumers” ( B = 0.236, z = 7.268), “local-based foodies” ( B = 0.238, z = 6.723) and “average consumers” ( B = 0.484, z = 12.638). However, households with more children were significantly less likely to be in the “high consumers” ( B = -1.000, z = -9.563) compared to households with fewer children. Compared to households with fewer teenagers, households with more teenagers were significantly more likely to be in the “local-based foodies” ( B = 0.128, z = 3.088) and “average consumers” ( B = 0.427, z = 9.558), whereas households with more teenagers were significantly less likely to be in the “high consumers” ( B = -0.558, z = -6.750). Compared to households with fewer adults, households with more adults were significantly more likely to be in the “average consumers” ( B = 0.430, z = 14.114) and significantly less likely to be in the “low consumers” ( B = -0.113, z = -4.536), “high consumers” ( B = -0.193, z = -6.031), “nomadic compensators” ( B = -0.161, z = -6.953). Finally, compared to households with fewer elderlies, households with more elderlies were significantly more likely to be in the “average consumers” ( B = 0.486, z = 9.779) and “local-based foodies” ( B = 0.216, z = 5.792) and significantly less likely to be in the “low consumers” ( B = -0.198, z = -5.507) and “nomadic compensators” ( B = -0.418, z = -11.851). [2] Other forms of public transportation (e.g., trams and metros) were not considered because of a data recording error on CBS. Thus, there was no available data for expenses on other public transportation options in 2015. However, the effect may be minimal because the transport monitor by the Netherlands Authority for Consumers and Markets reported over 76% of public transportation trips were made by trains and buses in 2019 (43). Since we would not expect a significant fluctuation in transportation patterns among the Dutch population, using expenditures on trains and buses is a sufficient proxy for expenditures on public transport. [3] The standard classification groups university graduates from universities of applied sciences and research universities altogether. However, we differentiated between the two because the broader classification results in an overly large category, which includes around 62% of our sample. Furthermore, graduates from the two universities receive different trainings. Whereas research universities offer research-oriented programmes focused on specific subjects, universities of applied sciences offer practical and profession-oriented trainings for students looking to enter a particular career type upon graduation. Therefore, separating these two groups allows us to have a more balanced and meaningful distinction in educational attainment. [4] There is a large proportion of households with no input of education levels because CBS does not have records of elderlies who had completed their education before the registration. Latent Gold performed mean imputations for households that had missing values in education levels (53). As a robustness check, we removed all households with missing values in education levels and ran the analysis. The results remain largely the same, with one exception in the independent variables. More information can be found in Appendix D. [5] The graph illustrates the relative difference within each indicator rather than absolute monetary values. For actual expenditure, refer to Appendix F. As shown in the table, while the visual representation may suggest a substantial difference in fish expenditures, the absolute difference in dairy expenditures is nearly twice as large. Therefore, the graph should be interpreted only in terms of relative differences within each indicator and not for cross-indicator comparisons. 4. Discussion The current study had two primary goals. The first was to gain new insights into the consumption profiles of the Dutch population by grouping households according to their similarities in household expenditures across five domains. The second goal was to delineate the role of SES components in predicting the probability of belonging to the identified profiles. 4.1 Consumption Profiles Regarding the first goal, we identified five consumption patterns/profiles: Low consumers, high consumers, nomadic compensators, local-based foodies, and average consumers. While we provide insights into distinct profiles, interpreting the psychological processes and individual motives underlying these profiles remains speculative. This is because a more detailed understanding of internal processes would require more detailed individual-level data collection. “Nomadic compensator” (31%) households demonstrated a dissonant consumption pattern that may align with the concept of moral licensing (36), such that households seemingly “compensated” their high consumption on flights by consuming relatively less in environmentally costly food-domains (e.g., meat and dairy products). Another possibility is that “nomadic compensators” households have occupations requiring frequent travel. Thus, their dissonant consumption tendency is not a lifestyle choice but an occupational necessity. Both reasons are likely, especially given that results from the multinomial logit indicated that households residing in urbanised regions and relatively highly educated households are more likely to be in the “nomadic compensators”. Most likely, this profile might even include both types of households and even others. By contrast, the “local-based foodies” (18%) shared a different dissonant consumption pattern in which their profile had high environmentally costly food consumption but low travel consumption. This may reflect a preference for investing in food quantity and quality while showing less interest in international or within-country travel. One possibility in explaining their tendencies to consume more locally on food is that households prefer familiarity or that they have long-established community ties to the local environment. An additional possibility is that households in the profile have reduced mobility due to physical constraints or health concerns. Both results are likely, as the results of the multinomial logit indicate that households with more elderlies are more likely to be in “local-based foodies” The “average consumer” (12%) households demonstrated a consumption pattern that closely aligned with the average of most consumption domains, except for the fact that they had the lowest consumption on residential utilities among the five profiles. The discrepancy could be because “average consumers" households may have a larger household size, leading to a lower per-capita consumption on utilities, as suggested by the results of the multinomial logits. The remaining two profiles demonstrated consistently high or low consumption patterns. “Low consumers” (27%) households demonstrated an overall low consumption on all domains, whereas “high consumers” (12%) households demonstrated an overall high consumption on all domains. These profiles were strongly related to lower and higher-income households, respectively. 4.2 SES on Consumption Regarding the second goal, we found significant main and interaction effects of education and income in predicting the probability of belonging to different profiles. Having an overall higher education level increases the likelihood of belonging to “nomadic compensators” and “average consumers”, while reducing the likelihood of belonging to “local-based foodies”. Conversely, having higher income decreases the likelihood of belonging to “local-based foodies”. Interestingly, the positive effect of income on predicting the likelihood of belonging to “high consumers” is stronger for households of a lower education level, as is the negative effect of income on predicting the likelihood of belonging to “low consumers”. However, the differences in consumption based on income vanish with higher education. Alternatively interpreted, the higher likelihood of belonging to “high consumers” with increasing education and the lower likelihood of belonging to “low consumers” with increasing education is more pronounced for households of a lower income, and the differences in consumption based on education vanish at high-income levels. Our results indicated a nuanced relationship between SES and consumption patterns. Conventionally, individuals with higher levels of education are expected to exhibit more environmentally friendly consumption patterns and lifestyles (e.g., low meat consumption and electricity usage). However, our results suggest that while households with a higher overall level of education tend to consume less in environmentally costly food domains, they exhibited higher consumption in flights and other forms of transportation—activities that could arguably be even more environmentally costly. Conversely, it is generally expected that individuals with lower levels of education have more environmentally costly consumption patterns (e.g., high meat consumption). While our findings confirm that households with lower education levels do spend more on environmentally costly food products, they tend to have lower travel-related consumption. Furthermore, while examining the interaction between income and education in predicting the likelihood of belonging to “high consumers” or “low consumers.” A higher income increased the probability of being categorised as a “high consumer” while reducing the likelihood of being a “low consumer.” However, this effect was stronger in lower-educated households, suggesting an interdependent relationship between income and education in shaping consumption patterns. 4.3 Contribution To our knowledge, the present research is the first to adopt a person-centered approach to understanding environmentally friendly consumption through LPA. We moved beyond the traditional variable-centered approach, which shifted the analytical focus from the behaviours to the consumers. The conceptual change of emphasis allowed us to examine the multidimensionality of consumption and inform us of the diversity regarding the consumption profiles in the Dutch population. The person-centered approach also opens several other intriguing questions for future investigations. First, given that consumption preferences are dynamic (56), future research could investigate how consumption profiles evolve over time and how changes in income and education influence shifts in different profiles via a latent transition analysis. Second, future studies could also apply the person-centered approach in different countries or cultural contexts to see if similar profiles emerge or if cultural factors significantly alter the relationships between education and income. Doing so can capture a more comprehensive and dynamic view of consumption that reflects real-world complexity and diversity, in turn, helping us tailor effective policy interventions responsive to cultural and demographic variations. Furthermore, we delineated the role of SES in consumption. The differential effects of SES on consumption profiles corroborate the theoretical supposition of previous literature that education and income may tap into different resources, thus impacting consumption differently. Our study demonstrates the importance of considering income and education as different dimensions of SES in understanding consumption. Future research could build on these findings by investigating the mechanisms underlying this moderation effect, such as environmental concerns and knowledge (8), pro-environmental norms (57), and other possible psychological variables such as self-regulation (58). Additionally, expanding the scope to consider other SES dimensions, such as occupation, wealth, and social capital, would provide a more comprehensive understanding of what drives consumption behaviours. These different directions for future research would build on and strengthen the understanding of the links between SES and consumption. Answers to these questions will greatly inform potential interventions to increase environmentally friendly consumption across all SES groups. Altogether, our study highlights the importance of cross-domain policy coordination as promoting sustainable consumption in one domain can cause unintended shifts in another, and these shifts vary depending on their SES. To provide just one suggestion on this, the relation between more extensive flight behaviours and more non-animal related food consumption for higher educated suggests compensation behaviours and, thus, if this group is pushed towards even less animal-related food consumption, they may feel entitled to fly more, which would have a counter-productive overall effect on CO 2 emissions. 4.4 Limitations Despite the encouraging findings, several limitations of our study should be noted. One limitation is the use of a relatively old dataset from 2015. The expenditure survey from Statistics Netherlands were conducted once every five years. Because of travel restrictions, we suspect that consumption, especially in relation to transportation, may differ significantly during and before COVID-19. Therefore, the 2015 cohort is the latest available data source that is most reflective of the typical consumption pattern of the population in the Netherlands. Still, the findings may not fully reflect the current consumption patterns as literature has shown that individuals have reported making modest to significant changes to their behaviours to live sustainably in the past decade (59), and they were actively buying more environmentally friendly products (e.g., sustainable clothing and plant-based milk) than they did five years ago (60). Therefore, future research is needed to validate the consumption profiles using more updated data sources. Second, estimating consumption patterns based on household expenditure is limited as expenditure does not necessarily equate to consumption. For instance, the same product but of a higher quality tends to be more expensive than cheaper options. Since higher expenditure automatically equates to more consumption according to our operationalisation, we may overestimate consumption and environmental impacts of wealthier households as wealthier households may tend to purchase higher quality, thus, more expensive goods and services. However, as argued in the carbon emission literature, the environmental impacts and emissions of wealthy households are not necessarily overestimated (60). The reason is that the exact carbon content of more expensive goods and services could not be compared to cheaper ones. For this reason, we may slightly overestimate the environmental impact of wealthier households. Future research can consider other methods, such as measuring the physical units consumed (i.e., quantity-based measures), to further improve the accuracy in estimating consumption patterns. Furthermore, our sample was not representative of all education levels, and it appeared to over-represent higher education levels, especially given how we handled missing data in education levels via mean imputations. Therefore, the findings of the current study might be applicable to households with higher education backgrounds. Future research is needed to assess generalisability using more representative data that covers households of all education levels. Declarations Ethics approval and consent to participate This study has been approved by the Ethics Committee of the Faculty of Social and Behavioural Sciences of Utrecht University [23-0555]. 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Available from: https://www.semanticscholar.org/paper/Technical-Guide-for-Latent-GOLD-5.1%3A-Basic%2C-and-1-Vermunt-Magidson/c7ec7772e43c2daf284d1f89902c21799baa9b7d Shah SS, Asghar Z. Dynamics of social influence on consumption choices: A social network representation. Heliyon. 2023 Jun 1;9(6):e17146. Byerly H, Balmford A, Ferraro PJ, Hammond Wagner C, Palchak E, Polasky S, et al. Nudging pro-environmental behavior: evidence and opportunities. Front Ecol Environ. 2018;16(3):159–68. Colombo SL, Chiarella SG, Lefrançois C, Fradin J, Simione L, Raffone A. Probing pro-environmental behaviour: A systematic review on its relationship with executive functions and self-regulation processes. J Environ Psychol. 2023 Dec 1;92:102153. Simon-Kucher. Global Sustainability Study [Internet]. 2021. Available from: https://www.simon- kucher.com/sites/default/files/studies/Simon-Kucher_Global_Sustainability_Study_2021.pdf Martins A. Business News Daily. 2024 [cited 2025 Feb 18]. Your Customers Prefer Sustainable Products - businessnewsdaily.com. Available from: https://www.businessnewsdaily.com/15087-consumers-want-sustainable-products.html Ameli N, Brandt N. Determinants of Households’ Investment in Energy Efficiency and Renewables: Evidence from the OECD Survey on Household Environmental Behaviour and Attitudes [Internet]. Paris: OECD; 2014 Dec [cited 2024 Feb 7]. Available from: https://www.oecd-ilibrary.org/economics/determinants-of-households-investment-in-energy-efficiency-and-renewables_5jxwtlchggzn-en BLS Report. Consumer Expenditures in 2013 [Internet]. 2015. Available from: https://www.bls.gov/opub/reports/consumer-expenditures/archive/consumer-expenditures-in-2013.pdf Baiocchi G, Minx J, Hubacek K. The Impact of Social Factors and Consumer Behavior on Carbon Dioxide Emissions in the United Kingdom: A Regression Based on Input−Output and Geodemographic Consumer Segmentation Data. J Ind Ecol. 2010 Jan;14(1):50–72. DEFRA. Distributional Impacts of Personal Carbon Trading A research report completed for the Department for Environment, Food and Rural Affairs by the Centre for Sustainable Energy. 2008 Mar. Kaza N. Urban form and transportation energy consumption. Energy Policy. 2020 Jan 1;136:111049. York R, Gossard MH. Cross-national meat and fish consumption: exploring the effects of modernization and ecological context. Ecol Econ. 2004 Mar 31;48(3):293–302. Schmidhuber J, Shetty P. Nutrition transition, obesity and noncommunicable diseases: Drivers, outlook and concerns. SCN News. 2005 Jan 1;29. Vanham D, Mak TN, Gawlik BM. Urban food consumption and associated water resources: The example of Dutch cities. Sci Total Environ. 2016 Sep 15;565:232–9. Nylund KL, Asparouhov T, Muthén BO. Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Struct Equ Model Multidiscip J. 2007 Oct 23;14(4):535–69. Hagenaars J, Mccutcheon A. Applied Latent Class Analysis Models. Can J Sociol Cah Can Sociol. 2003 Jan 1;28. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Discover Sustainability → Version 1 posted Editorial decision: Revision requested 22 Aug, 2025 Reviews received at journal 16 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 22 Jul, 2025 Reviewers invited by journal 22 Jul, 2025 Editor assigned by journal 14 Jul, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 11 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7101420","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489635149,"identity":"ac7528d3-24d2-4dc1-a85d-642c9d77e4f6","order_by":0,"name":"Joyce Ying-Ching Pang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYDADfjBZQIoWyQYQaUCKFoMDxGoxOMD8gLmg5o7d5hs5BswFxGlhM2CecexZ8jaQlhnEaeFhYOZhO5xsBtLCQ7yWf4eTjWeQpIW37bCdgQSxWiQPsxkcntl3OEHizLOCw0Rp4Tve/PBxwbfD9vztyRsf81QQoUXhMAMDEDEkNgCJA0RoYGCQByplBtL2RKkeBaNgFIyCkQkAzU8wlDUBMKQAAAAASUVORK5CYII=","orcid":"","institution":"Utrecht University","correspondingAuthor":true,"prefix":"","firstName":"Joyce","middleName":"Ying-Ching","lastName":"Pang","suffix":""},{"id":489635150,"identity":"16763032-ec30-4c32-b392-10f873935d42","order_by":1,"name":"Paulina Pankowska","email":"","orcid":"","institution":"Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"Paulina","middleName":"","lastName":"Pankowska","suffix":""},{"id":489635151,"identity":"ba550ed3-50b7-41cd-8136-27d9abd2b136","order_by":2,"name":"Félice Nunspeet","email":"","orcid":"","institution":"Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"Félice","middleName":"","lastName":"Nunspeet","suffix":""},{"id":489635152,"identity":"b866c817-1740-4bca-b0e0-7c2eb7f0e13a","order_by":3,"name":"Vincent Buskens","email":"","orcid":"","institution":"Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Buskens","suffix":""}],"badges":[],"createdAt":"2025-07-11 11:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7101420/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7101420/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-026-03186-w","type":"published","date":"2026-04-28T15:58:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87492951,"identity":"e384fc61-8d08-4277-8ed7-9387fc2e3353","added_by":"auto","created_at":"2025-07-24 12:20:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual Model of the Step Three Analysis in Our Study\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7101420/v1/06aa2b4985a6e2b409d7ec80.png"},{"id":87492952,"identity":"3bcdea15-33fc-41a5-a6cf-54756c50b6c6","added_by":"auto","created_at":"2025-07-24 12:20:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStandardised Mean Scores of Indicators for the Five-Profile Model\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7101420/v1/1711829969bcc559ce9e8f50.png"},{"id":87492958,"identity":"660695d7-4e08-4504-9682-b5552f5624d0","added_by":"auto","created_at":"2025-07-24 12:20:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEducation and Income on the Probability of Belonging to the “Low Consumers” Profile\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003eCentered income ranged from -1.87 to 1.77. The solid line at x = 1.5 represents the high-income level in which the difference in likelihood based on education becomes negligible after this point.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7101420/v1/bc42462030fe239fbd9dbb31.png"},{"id":87492954,"identity":"88cb334a-552f-4875-91fe-458a39ba90ae","added_by":"auto","created_at":"2025-07-24 12:20:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEducation and Income on the Probability of Belonging to the “High Consumers” Profile\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003eCentered income ranged from -1.87 to 1.77. The solid line at x = 1.5 represents the high-income level in which the difference in likelihood based on education becomes negligible after this point.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7101420/v1/641c92eea6d84089a5c3ba84.png"},{"id":108438030,"identity":"3299131e-dd87-48ca-aed5-9d5af5ef8f93","added_by":"auto","created_at":"2026-05-04 16:05:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":690229,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7101420/v1/53fa0ec2-3128-4855-bd51-10d63dddb925.pdf"},{"id":87494283,"identity":"1b668639-22f3-44dc-99c1-4c315d768035","added_by":"auto","created_at":"2025-07-24 12:36:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":274758,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7101420/v1/2dfbfb6cbbf73e4147744a41.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Five shades of green: Uncovering consumption profile heterogeneity and the role of socioeconomic status in the Netherlands","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMounting evidence has demonstrated that our current consumption patterns are surpassing the carrying capacity of the earth. For instance, world meat consumption has quadrupled since 1961 in absolute and per capita terms (1). Similarly, the average person buys 60% more clothes and keeps them for about half the time they did 15 years ago (2). Consequently, governments and organisations increasingly acknowledge the need to transform our existing consumption patterns to a more sustainable mechanism that induces people to consume less. However, before interception, it is important for us to first understand people\u0026rsquo;s consumption.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Although research is abundant in understanding consumption, most, if not all, studies adopt a variable-centered approach, which examines how specific factors, such as individual characteristics, shape consumption behaviours. For example, research has shown that socioeconomic status, an individual\u0026rsquo;s rank in society in terms of wealth, occupational prestige, and education (3), correlates positively with energy consumption (4-5). While this substantial line of research has significantly advanced our knowledge of the nature and determinants of different consumption behaviours, the variable-centered approach focused too much on consumption as \u003cem\u003evariables\u003c/em\u003e and not enough attention has been given to the \u003cem\u003econsumers\u003c/em\u003e who consume. Consequently, the variable-centered approach, which typically relies mostly on correlation and regression techniques, can say little about how multiple consumption behaviours are combined within consumers and how consumers with similar consumption profiles are represented in the population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile we acknowledge that there is a developing body of work in consumption research that adopts a person-centered approach, most, if not all, focus on a single consumption domain such as transportation (6) or food (7). To our knowledge, none has adopted the choice of a person-centered approach in simultaneously examining a comprehensive set of consumption domains. Thus, the study aims to address this question.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, we aim to advance our understanding of how SES influences consumption as contemporary studies mostly examined the influence of SES components in isolation or the influence of an aggregate SES index without examining whether the effects of SES components differ and whether the effect of one SES component\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e[1]\u003c/a\u003e (e.g., income) on consumption is contingent upon another component (e.g., education). Ample evidence demonstrates that the two components correspond to different resources. For instance, whereas education is a better index for cultural resources, income is a more direct index for material resources and purchasing power (8). Yet, little is known about how the two components differ in their effects on consumption and whether they interact to influence consumption jointly. We analysed the difference in income and education effects on various consumption domains. Furthermore, we explored how the impact of income on some types of consumption can be contingent upon people\u0026rsquo;s education level or, conversely, how the impact of education on consumption is contingent upon people\u0026rsquo;s income level. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study adopted a latent profile analysis (LPA), which is a multidimensional approach that allows us to classify households into meaningful, relatively homogenous groups according to their consumption. Furthermore, we observed whether SES, namely income and education, was associated with the identified profiles. We focused on SES because it is consistently associated with different types of consumption (4-5,9). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe aim to advance the literature on consumption in two ways. First, to our knowledge, this is the first study that applies LPA to the study of consumption. By moving beyond the variable-centred approaches, we offer a nuanced, multidimensional understanding of consumption. Second, we deepen the nuanced understanding by examining how SES components not only differ in their effects on consumption but also interact to affect consumption domains differently. By providing a comprehensive understanding of consumption, the findings of this study offer crucial insights in evaluating consumption patterns of different groups in terms of their sustainability. The comprehensive evaluation can then guide the development of integrated interventions in promoting socially responsible consumption that balance multiple sustainability goals and account for the diverse responses across income and education groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1 From a Variable-Centered to a Person-Centered Approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrently, the literature on consumption has adopted a variable-centered approach and mainly examines different domains of consumption separately. That is, many studies on consumption to date are limited to one or two specific dimensions of consumption in combination with one explanatory factor. For instance, consumption research focuses on the relationship between household income and water usage (10) and between education and clothes shopping (11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Even though these studies advance our knowledge of the antecedents within individual consumption domains, their predominant focus on isolated consumption domains has ignored the inherently interconnected and multidimensional nature of consumption. In real-life contexts, consumers make a series of decisions in which one choice follows another. Indeed, the spillover literature from environmental psychology corroborates the notion that engaging in one behaviour affects the probability of engaging or disengaging in a second behaviour (12). For example, individuals engage in otherwise environmentally harmful behaviours after having engaged in prior environmentally friendly actions (\u003cem\u003enegative spillover\u003c/em\u003e). Alternatively, individuals may engage in more environmentally friendly behaviours after performing an environmentally friendly action (\u003cem\u003epositive spillover\u003c/em\u003e). While several scholars have acknowledged the multidimensionality of consumption (13\u0026ndash;16), most existing research still limits its scope to one or two domains, seldom addressing multiple consumption areas simultaneously.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother reason to examine multiple consumption domains simultaneously is that the influence of consumption predictors may vary across domains. For instance, not all consumption behaviours are subjected to normative influence to a similar extent, partly because they differ in how visible they are to others. Individuals may know which of their neighbours bike to work, but they may not know which neighbours are taking longer showers. Following this logic, it is plausible that visible consumption (e.g., clothes purchase) is more likely to be subjected to normative influence when compared to less visible consumption (e.g., water usage), even for people who genuinely have a pro-environmental stance as they can then signal their stance as an environmentalist to others (that is, the so-called \u0026ldquo;green to be seen\u0026rdquo; phenomenon) (17). Notably, visibility may not always lead to reduced consumption as people having a counter-environmental stance may consider pro-environmental behaviours as signalling an unwanted social identity of \u0026ldquo;environmentalist\u0026rdquo;, in turn, leading to more consumption (that is, the so-called \u0026ldquo;brown to keep down\u0026rdquo; phenomenon) (17). To that end, we ought to consider consumption behaviours of varying levels of visibility to paint a comprehensive consumption profile. In our study, for instance, meat consumption is a visible form of consumption whereas water usage is a less visible one.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbove all, examining isolated consumption behaviours may also lead to an inaccurate estimation of people\u0026rsquo;s consumption on the extent to which one is environmentally friendly. The misrepresentation can hinder efforts to promote sustainable consumption, as it may lead to targeting the wrong groups or domains with interventions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the potential of a person centered-approach in consumption, empirical evidence is still lacking in this area. Therefore, the first aim of the study is to build consumption profiles using a multiplicity of consumption behaviours concerning fashion, food, transport, water, and energy, as they are the primary sources of individual carbon contribution (18). Since this is, to our knowledge, the first study that applies LPA on multiple consumption domainsvq, the type and number of profiles that would emerge in the analysis is unknown. To that end, no hypotheses regarding the profiles are made.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 SES and Consumption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe second aim of the study is to delineate the role of SES in consumption. SES is typically measured by income, educational attainment, or their combinations (19). For instance, scholars have documented a positive correlation between income and energy consumption (4-5) and a negative correlation between education and meat consumption (9,20). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile research on the effects of separate SES components or on an aggregate SES index on consumption is abundant, comparatively little is known about their isolated effects and their interplay, that is, whether the effects of different components differ on having a certain consumption profile and whether the effect of one SES component (e.g., income) on consumptions is contingent upon another components (e.g., education). However, there is good conceptual reason to consider the effects and interplay of education and income level as they tap into different resources and opportunities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDrawing from Bourdieu\u0026rsquo;s capital theory (21), income and education correspond to two different resources, which, in turn, shape consumption in different ways. Income reflects economic capital, a more direct index for material resources and purchasing power, whereas education reflects cultural capital, a better index for cultural resources (8,21). Since sustainability issues are usually grounded in the language of scientific knowledge (22), higher-educated individuals may value such messages to a greater extent than people with a lower education level, as higher-educated individuals frequently cite and rely on expert knowledge in their tertiary education and are also taught to value such expert knowledge over other sources of knowledge such as tradition, kin, or peer groups (23). As a result, higher-educated individuals may be more aware of the potential environmental consequences of their actions and more likely to acknowledge their personal responsibility to be environmentally friendly, in turn, resulting in lower consumption.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to the provision of different resources, the social norms associated with environmentalism may vary as a function of income and education level. One common perception people have of environmentalists is that they are highly educated (24). Given the stereotype about environmentalists, higher-educated individuals may view pro-environmental norms as an in-group norm, whereas individuals with a high-income level may not. According to self-categorisation theory (25), normative influence is stronger when the norms are associated with self-relevant groups, in turn, higher-educated individuals may have lower consumption because of the highly self-relevant pro-environmental norms. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing meat consumption as an illustration, one with high income and education may have more financial capability to afford meat products. However, they may still consume relatively less meat because they are more aware of the environmental damage and health consequences brought by (excessive) meat consumption. Conversely, one with high income and low education may consume more meat because they may have less exposure to scientific knowledge about the negative environmental consequences of meat consumption and experience less normative pressure to behave environmentally friendly. Therefore, individuals with high income and low education can afford meat products while facing fewer constraints. Therefore, meat purchases may be linked to financial resources, but possibly less for people with a higher education level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSuch a supposition also applies to other consumption domains, such as energy usage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies on the relationship between education level and energy-efficient investments yielded inconsistent results. While some studies found no correlation (26-27), some documented a positive correlation (28\u0026ndash;30). Though it has yet to be empirically examined, a possible explanation proposed by scholars is that higher educated people tend to have higher income levels, which increases energy use (31-32), but also the probability of purchasing energy-efficient investments (27,31). The explanation suggests that the effect of education on the purchase of energy-efficient investments is contingent upon people\u0026rsquo;s income level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Heterogeneous Effects of SES on Consumption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotably, we do not argue that individuals with high income and education will always result in low consumption across all domains. For instance, empirical studies have consistently found that the probability of travelling by plane increases with education and income level (33\u0026ndash;35). A possible explanation would be that individuals of higher education levels have an increased need to participate in culturally enriching activities to satisfy their taste of culture and aesthetic pleasures (21). Thus, they may have increased travel and fashion expenditures despite a high level of environmental concern. Alternatively, drawing from the moral licensing literature in environmental psychology, another possible explanation is that individuals with high income and education feel morally justified to behave in an environmentally unfriendly manner in certain domains because they have had low consumption in other domains (36). For instance, among individuals with a high level of environmental concern, those reminded of their energy-efficient investments reported lower motivation to reduce air travel (37).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn fact,\u0026nbsp;we do not argue that the same combination of income and education will always result in homogeneous effects across consumption domains. It is possible that consumption behaviours across different domains are sometimes uncorrelated. On the other hand, when they are correlated, consumption behaviour in one domain influences consumption behaviour in another, either negatively (\u003cem\u003enegative spillover\u003c/em\u003e) or positively (\u003cem\u003epositive spillover\u003c/em\u003e). For instance, researchers found a negative relationship between household water use and electricity consumption (38). Nonetheless, scholars also documented a positive relationship between fuel-efficient driving styles and intentions to reduce meat consumption (39). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile there has been research on the role of identity and situational factors in increasing or decreasing the likelihood of positive and negative spillover effects (29,40), it remains unknown how people\u0026rsquo;s SES may influence the directionality of spillover. There are hints that environmental concern, which relates to people\u0026rsquo;s education level, plays a role in determining the directionality, yet results have been inconsistent (29,37,40\u0026ndash;42). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAltogether, the discussion suggests that there may be notable differences between income and education in predicting consumption and that the two may interact to influence consumption jointly. Given the compiled arguments and evidence, no straightforward predictions are possible regarding the direction of the relationships between SES components and consumption behaviours. Therefore, this study is an exploratory study as we cannot predict which profiles would emerge and how SES would influence the probability of belonging to any identified profiles. Hence, no hypotheses were proposed, and we posed the following research questions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRQ1.\u003c/em\u003e \u003cem\u003eWhat consumption profiles exist in the population of the Netherlands concerning consumption behaviours related to fashion, food, transportation, water, and energy?\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRQ2.\u003c/em\u003e \u003cem\u003eHow does the probability of belonging to any consumption profile depend on people\u0026rsquo;s income and education level?\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e[1]Although many more SES components can be distinguished (e.g., cultural capital, wealth, occupation), income and education are two of the most commonly used metrics of SES and are the most comprehensively available in the data we utilised in the present study. Therefore, we focused on the role of income and education in this paper.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data\u003c/h2\u003e\u003cp\u003eTo answer RQ1, we used survey data from the Dutch Centraal Bureau voor de Statistiek (CBS) to identify consumption patterns in the Dutch population. We mainly utilised the expenditure survey (budgetonderzoek), in which participating households record all monthly expenses exceeding \u0026euro;20 and weekly expenses under \u0026euro;20. At year\u0026rsquo;s end, they report significant expenses, such as a new car or vacations. The survey is conducted every five years, and we used the expenditure survey from the 2015 cohort (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14,404) as it represents the most recent version of pre-COVID data.\u003c/p\u003e\u003cp\u003eTo understand how income and education affect the probability of belonging to identified consumption profiles (RQ2), we matched participating households in the expenditure survey with their corresponding register data on the education level through a unique anonymised identifier. Register data on education (hoogsteopltab), provided by Dutch education authorities, contains the highest attained education level of the individuals. Household income is an existing variable in the expenditure survey. Additionally, we included household ownership, the degree of urbanisation to which a household belongs, and household composition as control variables when estimating the relationship between SES and consumption profiles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Consumption Behaviours\u003c/h2\u003e\u003cp\u003eConsumption behaviours were measured using eleven indicators (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Since we would likely overestimate the consumption of larger households as they inevitably have more expenditures, we recalculated a per capita household expenditure by dividing the total household expenditure by the number of people living in the household.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eTen Indicators that Covered Five Targeted Consumption Domains\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTypes of consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer capita household expenditure on\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Meat\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Fish\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(Continued.)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTypes of consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer capita household expenditure on\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Dairy products\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Vegetables\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTravel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Flights\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) A constructed variable for expenditures on private transport, which is a sum of expenditures on fuels (including petrol, diesel, and other fuels) and taxis.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) A constructed variable for expenditures on public transport, which is a sum of expenditures on trains and buses\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFashion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) Clothes and shoes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) Electricity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Gas\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) Water usage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe selection of indicators was based on several considerations. Namely, the selected indicators were diverse and represented the five consumption domains targeted in our study. Further, as indicators with high response variability are more useful in distinguishing different consumption patterns, indicators with (very) low variance were excluded from the analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Socioeconomic Status (SES)\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Household Income\u003c/h2\u003e\u003cp\u003eTaken from the expenditure survey, we operationalised household income as the mean-centered annual disposable income of a household, which was treated as a continuous variable in our study. A household\u0026rsquo;s disposable income is the gross household income minus any income transfers such as health insurance and income taxes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Education\u003c/h2\u003e\u003cp\u003eResearch has shown that taking the highest education level among parents to represent the family does not provide an accurate influence of education on household outcomes (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Therefore, based on the register data on education, we operationalised the education level of a household by obtaining the mode of the education level of people who are above 25 years old in a household, as they are more likely to complete their education and are conventionally the ones who make decisions for the household. Such approach is also more commonly used in operationalising household SES in family sociology (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUsing the international standard classification of education as the basis\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e, we distinguished between four levels of education: low, medium, high, and very high.\u003c/p\u003e\u003cp\u003eThe low education level includes groups 1 through 8 (all years) of primary and special primary education plus the first three years of senior general secondary education (HAVO) and pre-university secondary education (VWO); the various pathways of prevocational secondary education (VMBO) including lower secondary vocational training and assistant\u0026rsquo;s training (MBO-1).\u003c/p\u003e\u003cp\u003eThe medium education level includes upper secondary education (HAVO/VWO), basic vocational training (MBO-2), vocational training (MBO-3), and middle management and specialist education (MBO-4).\u003c/p\u003e\u003cp\u003eThe high education level refers to associate degree programmes, bachelor\u0026rsquo;s programs, and master\u0026rsquo;s programs at Universities of Applied Sciences (HBO).\u003c/p\u003e\u003cp\u003eThe very high education level refers to bachelor\u0026rsquo;s, master\u0026rsquo;s, and doctoral programs at research universities (WO).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Control Variables\u003c/h2\u003e\u003cp\u003eAdditionally, we controlled for three variables, namely household ownership, household composition, and the degree of urbanisation, as literature has shown that they are related to consumption. Theoretical justifications for each control variable can be found in Appendix A.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1 Household Ownership\u003c/h2\u003e\u003cp\u003eTaken from the expenditure survey, household ownership originally had three levels: rented, rented with rental allowance, and own property. For the sake of simplicity, we recoded the variable so that it only distinguishes between rented vs. owned property in our analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2 Household Composition\u003c/h2\u003e\u003cp\u003eTaken from the register database (Gbapersoontab), which contains microdata on the personal characteristics of individuals registered in the Personal Records Database (BasisRegistratie Personen; BRP), we coded the total number of children (0\u0026ndash;12 years old), teenagers (13\u0026ndash;18 years old), adults (\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62 CR63 CR64 CR65\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), and elderlies (\u0026gt;\u0026thinsp;67) in a household.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3 The Degree of Urbanisation\u003c/h2\u003e\u003cp\u003eTaken from the expenditure survey, we utilised an existing categorical variable that measures the concentration of human activities based on the average environmental address density. The surrounding address density was classified into five categories: not urbanised, with fewer than 500 addresses per square kilometre; hardly urbanised, with 500 to 1,000 addresses per square kilometre; moderately urbanised, with 1,000 to 1,500 addresses per square kilometre; strongly urbanised with 1,500 to 2,500 addresses per square kilometre; and extremely urbanised, with 2,500 addresses or more per square kilometre.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data cleaning\u003c/h2\u003e\u003cp\u003eTo handle extreme values in the income variable and expenditures, we identified outliers using the Tukey\u0026rsquo;s method (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), which defines outliers as observations that exceed 1.5 times the interquartile range (IQR) beyond the first quartile (Q1) or above the third quartile (Q3). We winsorised the outliers by replacing values below this threshold with Q1 and values above this threshold with Q3. This approach allows us to reduce the impact of extreme values while preserving the overall distribution of the data.\u003c/p\u003e\u003cp\u003eFurthermore, all negative expenditures, which likely indicate recovered money, such as goods sold and returns (e.g., a negative meat expenditure could mean that the households earn money by selling meat), were coded to 0. The recode ensured our analysis remains solely focused on understanding household consumption behaviours without interference from values that may represent income or productive activities. The occurrence of negative expenditures was rare, accounting less than 2% across all indicators. All data preparation and cleaning were performed using the R statistical computing software 4.4.0 (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Data Analysis\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e2.6.1 RQ1 Consumption Patterns\u003c/h2\u003e\u003cp\u003eThe first step of the analysis focused on identifying different consumption patterns among Dutch households in 2015 using latent profile analysis (LPA). The analysis was conducted in Latent Gold version 6.0 (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLPA is used to identify latent unobserved variables that explain interrelations between a set of observed indicators. Put differently, LPA uncovers hidden groupings or sub-populations based on observed data (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). It has a wide range of applications, including identifying political preferences (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), understanding different personality types or traits (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), and discovering consumption patterns in isolated domains (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs the number of profiles was not known a priori, several models were estimated with the number of profiles ranging from one to ten. The final model was then selected based on two sets of criteria, including statistical (i.e., the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC)) and substantive considerations, i.e., interpretability, theoretical relevance, and profile size (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2 RQ2: The Effect of SES on Consumption Profiles\u003c/h2\u003e\u003cp\u003eThe second part of the analysis tested how SES components (independent variables) affected the probability of belonging to the resultant consumption profiles.\u003c/p\u003e\u003cp\u003eIn our analysis, we used a bias-adjusted three-step approach (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). In the first step, we used a latent profile model that can formalised as,\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:P\\left(Y=y\\right)=\\:\\sum\\:_{x=1}^{k}P\\left(X=x\\right)P\\left(Y=y\\:\\right|X=x)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P(Y=y)\\)\u003c/span\u003e\u003c/span\u003e denotes the probability of observing a specific pattern of consumption across the ten indicators considered. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P(X=x)\\)\u003c/span\u003e\u003c/span\u003e then indicates the probability of belonging to a specific latent profile \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e indicates the number of profiles. Finally, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left(Y=y|X=x\\right)\\)\u003c/span\u003e\u003c/span\u003e indicates the probability of observing a specific pattern of responses \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y\\)\u003c/span\u003e\u003c/span\u003e given profile membership \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e. In the second step, the estimated individual-level profile membership was saved in the data, and in the third step, we used a multinomial logistic regression model to estimate how income, education, and their interaction (independent variables) predict the probability of belonging to the obtained profiles (dependent variables), while considering other control variables. In doing so, we used a correction to account for the uncertainty of class membership (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). The model used in the analysis can also be graphically explained as follows (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo answer RQ1, we used survey data from the Dutch Centraal Bureau voor de Statistiek (CBS) to identify consumption patterns in the Dutch population. We mainly utilised the expenditure survey (budgetonderzoek), in which participating households record all monthly expenses exceeding \u0026euro;20 and weekly expenses under \u0026euro;20. At year\u0026rsquo;s end, they report significant expenses, such as a new car or vacations. The survey is conducted every five years, and we used the expenditure survey from the 2015 cohort (\u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 14,404) as it represents the most recent version of pre-COVID data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo understand how income and education affect the probability of belonging to identified consumption profiles (RQ2), we matched participating households in the expenditure survey with their corresponding register data on the education level through a unique anonymised identifier. Register data on education (hoogsteopltab), provided by Dutch education authorities, contains the highest attained education level of the individuals. Household income is an existing variable in the expenditure survey. Additionally, we included household ownership, the degree of urbanisation to which a household belongs, and household composition as control variables when estimating the relationship between SES and consumption profiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Consumption Behaviours\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsumption behaviours were measured using\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eeleven indicators (Table 1). \u0026nbsp; Since we would likely overestimate the consumption of larger households as they inevitably have more expenditures, we recalculated a per capita household expenditure by dividing the total household expenditure by the number of people living in the household.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eTen Indicators that Covered Five Targeted Consumption Domains\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypes of consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer capita household expenditure on\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eFood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(1) Meat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(2) Fish\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e(Continued.)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypes of consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer capita household expenditure on\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(3) Dairy products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(4) Vegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eTravel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(5) Flights\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(6) A constructed variable for expenditures on private transport, which is a sum of expenditures on fuels (including petrol, diesel, and other fuels) and taxis.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(7) A constructed variable for expenditures on public transport, which is a sum of expenditures on trains and buses[2]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eFashion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(8) Clothes and shoes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eEnergy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(9) Electricity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(10) Gas\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eWater\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e(11) Water usage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe selection of indicators was based on several considerations. Namely, the selected indicators were diverse and represented the five consumption domains targeted in our study. Further, as indicators with high response variability are more useful in distinguishing different consumption patterns, indicators with (very) low variance were excluded from the analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Socioeconomic Status (SES)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3.1 Household Income\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eTaken from the expenditure survey, we operationalised household income as the mean-centered annual disposable income of a household, which was treated as a continuous variable in our study. A household\u0026rsquo;s disposable income is the gross household income minus any income transfers such as health insurance and income taxes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3.2 Education\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch has shown that taking the highest education level among parents to represent the family does not provide an accurate influence of education on household outcomes (44). Therefore, based on the register data on education, we operationalised the education level of a household by obtaining the mode of the education level of people who are above 25 years old in a household, as they are more likely to complete their education and are conventionally the ones who make decisions for the household. Such approach is also more commonly used in operationalising household SES in family sociology (45).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing the international standard classification of education as the basis[3], we distinguished between four levels of education: low, medium, high, and very high.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe low education level includes groups 1 through 8 (all years) of primary and special primary education plus the first three years of senior general secondary education (HAVO) and pre-university secondary education (VWO); the various pathways of prevocational secondary education (VMBO) including lower secondary vocational training and assistant\u0026rsquo;s training (MBO-1).\u003c/p\u003e\n\u003cp\u003eThe medium education level includes upper secondary education (HAVO/VWO), basic vocational training (MBO-2), vocational training (MBO-3), and middle management and specialist education (MBO-4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe high education level refers to associate degree programmes, bachelor\u0026rsquo;s programs, and master\u0026rsquo;s programs at Universities of Applied Sciences (HBO).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe very high education level refers to bachelor\u0026rsquo;s, master\u0026rsquo;s, and doctoral programs at research universities (WO).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Control Variables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, we controlled for three variables, namely household ownership, household composition, and the degree of urbanisation, as literature has shown that they are related to consumption. Theoretical justifications for each control variable can be found in Appendix A.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4.1 Household Ownership\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaken from the expenditure survey, household ownership originally had three levels: rented, rented with rental allowance, and own property. For the sake of simplicity, we recoded the variable so that it only distinguishes between rented vs. owned property in our analysis. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4.2 Household Composition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaken from the register database (Gbapersoontab), which contains microdata on the personal characteristics of individuals registered in the Personal Records Database (BasisRegistratie Personen; BRP), we coded the total number of children (0-12 years old), teenagers (13-18 years old), adults (19-66), and elderlies (\u0026gt;67) in a household.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4.3 The Degree of Urbanisation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaken from the expenditure survey, we utilised an existing categorical variable that measures the concentration of human activities based on the average environmental address density. The surrounding address density was classified into five categories: not urbanised, with fewer than 500 addresses per square kilometre; hardly urbanised, with 500 to 1,000 addresses per square kilometre; moderately urbanised, with 1,000 to 1,500 addresses per square kilometre; strongly urbanised with 1,500 to 2,500 addresses per square kilometre; and extremely urbanised, with 2,500 addresses or more per square kilometre.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Data cleaning\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo handle extreme values in the income variable and expenditures, we identified outliers using the Tukey\u0026rsquo;s method (46), which defines outliers as observations that exceed 1.5 times the interquartile range (IQR) beyond the first quartile (Q1) or above the third quartile (Q3). We winsorised the outliers by replacing values below this threshold with Q1 and values above this threshold with Q3. This approach allows us to reduce the impact of extreme values while preserving the overall distribution of the data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Furthermore, all negative expenditures, which likely indicate recovered money, such as goods sold and returns (e.g., a negative meat expenditure could mean that the households earn money by selling meat), were coded to 0. The recode ensured our analysis remains solely focused on understanding household consumption behaviours without interference from values that may represent income or productive activities. The occurrence of negative expenditures was rare, accounting less than 2% across all indicators. All data preparation and cleaning were performed using the R statistical computing software 4.4.0 (47). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Data Analysis\u0026nbsp;\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.6.1 RQ1 Consumption Patterns\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first step of the analysis focused on identifying different consumption patterns among Dutch households in 2015 using latent profile analysis (LPA). The analysis was conducted in Latent Gold version 6.0 (48).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLPA is used to identify latent unobserved variables that explain interrelations between a set of observed indicators. Put differently, LPA uncovers hidden groupings or sub-populations based on observed data (49). It has a wide range of applications, including identifying political preferences (50), understanding different personality types or traits (51), and discovering consumption patterns in isolated domains (52).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs the number of profiles was not known a priori, several models were estimated with the number of profiles ranging from one to ten. The final model was then selected based on two sets of criteria, including statistical (i.e., the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC)) and substantive considerations, i.e., interpretability, theoretical relevance, and profile size (49).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.6.2 RQ2: The Effect of SES on Consumption Profiles\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe second part of the analysis tested how SES components (independent variables) affected the probability of belonging to the resultant consumption profiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our analysis, we used a bias-adjusted three-step approach (48,53). In the first step, we used a latent profile model that can formalised as, \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Descriptive Statistics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTable 2 provides an overview of the means and standard deviations of the eleven consumption indicators used in the LPA, as well as the proportions of the main independent variables that influence profile membership. Information regarding the control variables in the model can be found in Appendix B. A correlation matrix of the eleven indicators can be found in Appendix C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eSummary Statistics for the Main Variables in the Analysis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eMeat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e261.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e165.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eFish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e35.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e44.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eDairy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e172.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e106.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eVegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e201.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e113.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eFashion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e555.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e484.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e(Continued.)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eFlights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e53.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e70.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ePrivate transportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e572.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e376.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ePublic transportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e9.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eElectricity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e259.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e99.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eGas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e483.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e189.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e116.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e32.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e32226.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e18469.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eHousehold education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e5.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eMedium\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e19.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e23.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eVery High\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e16.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eMissing values[4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e34.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;We can observe high variability in all indicators apart from water expenditures. Further, all indicators correlate with one another significantly, except for the correlation between private and public transportation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Identifying Consumption Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe next subsection highlights the steps undertaken in the LPA to identify consumption patterns and present the results. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2.1 Model Selection\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eModels, with the number of profiles \u0026mdash; \u003cem\u003ek\u0026nbsp;\u003c/em\u003e\u0026mdash;varying from one to ten, were considered. The selection of the best-fitting model was made based on statistical criteria, that is, the model fit expressed using BIC, AIC, and substantive criteria, that is, profile interpretability and size (49). Because of model convergence issues, we adopted a stepwise approach in selecting the best-fitting model. We first focused on the selection of the optimal number of profiles based on the most parsimonious model with respect to the assumptions regarding the variance-covariance matrix (namely, equal variance and zero covariance). Upon selection, we started relaxing the variance assumption for one indicator at a time to determine whether freeing a specific indicator improved the model fit substantially. In the end, we selected the five-profile model with varying variances for three indicators (fish, flights, and public transportation) for further substantive analyses. We chose to retain the zero-covariance assumption. Overall, we had a good quality of classification, with entropy \u003cimg width=\"18\" height=\"19\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABIAAAATCAMAAACqTK3AAAAAAXNSR0IArs4c6QAAAGBQTFRFAAAAAAAAAAA6ADpmADqQAGa2OgAAOjqQOmaQOma2OpC2OpDbZgAAZrbbZrb/kDoAkDpmkLbbkLb/kNv/tmYAtv//25A625Bm27Zm27aQ2////7Zm/9uQ/9u2//+2///bqocxEQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAe0lEQVQoU5VQSRKAIAwrLriLKyqi/P+XtnWGQceLOfSQJmkA4DdWKXLzcNnMHHX7DnLdTJQWiKjh7XKLnEqRj0Zihtt2kt9KHKjZWbaTgIaVmMCUTgxssgguOYW7uA9vn3UFK4d7UArRASaM4h4eWJRqifL5uN//8224ABggBT1gfU+5AAAAAElFTkSuQmCC\" alt=\"image\"\u003e\u0026nbsp;= 0.973. The precise steps in selecting the best-fitting model can be found in Appendix E.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 The Five-Profile Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 displays the centered mean scores on the eleven indicators. All indicators were standardised to a mean of 0 and a standard deviation of 1 to facilitate comparability and interpretation of the profiles[5].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe first profile, which included 27% of the sample, was characterised by an overall low expenditure as the means of all indicators were lower in this profile than in others. This profile was accordingly labelled as \u0026ldquo;\u003cem\u003elow consumers\u003c/em\u003e\u0026rdquo;. Conversely, the second profile, which included 12% of the sample, was characterised by an overall high expenditure across all indicators. We termed the second profile \u0026ldquo;\u003cem\u003ehigh consumers\u003c/em\u003e\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe third profile, which amounted to 31% of the sample, was the largest of all five profiles and shared a high expenditure on flights and public transportation but a low expenditure on all food-related indicators. The third profile demonstrated a dissonant consumption pattern, which may appear paradoxical. While households in the third profile spent less on environmentally costly food (e.g., dairy products and meat)\u0026mdash; suggesting environmentally friendly behaviours\u0026mdash; they simultaneously had high expenditures on flights, a notably environmentally harmful costly activity. The contrast led to the profile being labelled \u0026ldquo;nomadic compensators\u0026rdquo;, capturing the dissonance between their environmentally friendly food preferences and carbon-intensive travel behaviours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, the fourth profile, comprising about 18% of the sample, displayed an opposing yet similarly dissonant pattern. Households in this profile spent relatively more on food, including the environmentally costly categories like dairy and meat, while keeping travel expenses low. It appears that these households tend to spend more locally and focus on food, therefore, this profile was labelled \u0026ldquo;local-based foodies\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eThe final profile included about 12% of the sample. Households in this profile exhibited consumption patterns that closely aligned with the average across most domains apart from residential utilities, which was low electricity, gas, and water consumption when compared to other profiles. Because the expenditures for most indicators hovered around the mean, we termed this profile \u0026ldquo;\u003cem\u003eaverage consumers\u003c/em\u003e\u0026rdquo;. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3.1 Descriptives per Profile\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross the profiles, \u0026ldquo;high consumers\u0026rdquo; had the highest income, followed by \u0026ldquo;average consumers\u0026rdquo;, \u0026ldquo;nomadic compensators\u0026rdquo;, \u0026ldquo;local-based foodies\u0026rdquo;, and \u0026ldquo;low consumers\u0026rdquo;. When examining education levels, \u0026ldquo;average consumers\u0026rdquo; had the highest proportion of households with a very high level of education, followed by \u0026ldquo;nomadic compensators\u0026rdquo;, \u0026ldquo;high consumers\u0026rdquo;, \u0026ldquo;local-based foodies\u0026rdquo; and \u0026ldquo;low consumers\u0026rdquo;. Refer to Appendix G for the detailed figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 The Probability of Belonging to Different Profiles\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the final part of the analysis, we used multinomial logistic regression to predict the probability of belonging to the identified profiles as a function of education, income, and control variables. Effect coding was used in the analyses, and we assessed whether a given characteristic increases or decreases the likelihood of belonging to a particular profile relative to the average likelihood of profile membership across all profiles. See Table 3 for the statistical significance of the SES and control variables on profile memberships.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eConsumption Profiles: Effects of SES and Control Variables on Profile Memberships\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLow consumers\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHigh consumers\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNomadic compensators\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLocal-based foodies\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAverage consumers\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ez-score\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ez-score\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ez-score\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ez-score\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ez-score\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eEducation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-9.993*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.528*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-8.512*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.463*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-15.257*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15.427*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-4.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eInteraction education income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3.526*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-2.561*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eHousehold ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-4.563*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eDegree of urbanisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-10.201*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.869*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.248*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-6.081*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eNo. of children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e7.268*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-9.563*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.723*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12.638*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eNo. of teenagers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-6.750*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.088*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9.558*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eNo. of adults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-4.536*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-6.031*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-6.953*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e14.114*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eNo. of elderlies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-5.507*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-11.851*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.792*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9.779*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eThe \u003cem\u003eB\u003c/em\u003es represent unstandardised coefficients.\u003cem\u003e\u0026nbsp;\u003c/em\u003e* indicates significant z-score in which the z-score falls beyond the range of -1.96 to 1.96, thus, the variable is significant in predicting the probability of belonging to a certain profile. Income was mean-centered for analyses and ranged from -1.87 to 1.77. A negative coefficient indicates a lower probability of belonging to that profile relative to the average probability across all profiles, while a positive coefficient suggests a higher probability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4.1 SES variables\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEducation effects show that households with an overall high level of education were significantly more likely to be in \u0026ldquo;nomadic compensators\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.123, \u003cem\u003ez\u003c/em\u003e = 5.528) and \u0026ldquo;average consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.149, \u003cem\u003ez\u003c/em\u003e = 4.463), whereas households with an overall high level of education were significantly less likely to be in \u0026ldquo;local-based foodies\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.226, \u003cem\u003ez\u003c/em\u003e = -8.512) compared to other types of households.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe income effect showed that households with more income were significantly less likely to be in the local-based foodies (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.127, \u003cem\u003ez\u003c/em\u003e = -4.013) compared to other types of households.\u003c/p\u003e\n\u003cp\u003eIn the case of \u0026ldquo;low consumers\u0026rdquo; and \u0026ldquo;high consumers\u0026rdquo;, the effect of income (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.444, \u003cem\u003ez\u003c/em\u003e = -15.257; \u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.548, \u003cem\u003ez\u003c/em\u003e = 15.427, respectively) and education (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.230, \u003cem\u003ez\u003c/em\u003e = -9.993; \u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.183, \u003cem\u003ez\u003c/em\u003e = 5.008, respectively) depended on each other. We observed significant interactions between income and education in predicting the probability of belonging to \u0026ldquo;low consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.114, \u003cem\u003ez\u003c/em\u003e = 3.526) and \u0026ldquo;high consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.111, \u003cem\u003ez\u003c/em\u003e = -2.561). To provide a better understanding of the interactions, we visualised the interactions in the following graphs. Figure 3 shows how the interaction between education and income influences the probability of belonging to \u0026ldquo;low consumers\u0026rdquo;, whereas Figure 4 shows how the interaction between education and income influences the probability of belonging to \u0026ldquo;high consumers\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3 shows that households with less income or with a lower education level are more likely to be \u0026ldquo;low consumers\u0026rdquo;, whereas Figure 4 indicates households with more income or with a higher education level are more likely to be in \u0026ldquo;high consumers\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMore importantly, the effect of income on the probability of belonging to \u0026ldquo;low consumers\u0026rdquo; and \u0026ldquo;high consumers\u0026rdquo; was moderated by the education level (see Figures 3 and 4, respectively). While higher income decreased the likelihood of being in a \u0026ldquo;low consumers\u0026rdquo; profile and increased the likelihood of being in a \u0026ldquo;high consumer\u0026rdquo; profile, this effect was stronger in households with lower education levels. However, the difference in likelihood based on education vanished at high-income levels. At high-income levels, households of all education levels shared a similarly low and high probability of belonging to \u0026ldquo;low consumers\u0026rdquo; and \u0026ldquo;high consumers\u0026rdquo;, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4.2 Control Variables\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Given the exploratory nature of the paper, we further explored how several control variables predict the probability of belonging to different profiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold Ownership.\u0026nbsp;\u003c/strong\u003e Households who purchased their home were significantly less likely to be in the nomadic consumer profile (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.187, \u003cem\u003ez\u003c/em\u003e = -4.563) compared to households who rented their home.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDegree of Urbanisation.\u003c/strong\u003e Households living in more urbanised regions were significantly more likely to be in the \u0026ldquo;high consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.094, \u003cem\u003ez\u003c/em\u003e = 4.869), \u0026ldquo;nomadic compensators\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.054, \u003cem\u003ez\u003c/em\u003e = 4.248), and \u0026ldquo;average consumers\u0026rdquo; profiles (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.077, \u003cem\u003ez\u003c/em\u003e = 4.014). Conversely, households living in more urbanised regions were significantly less likely to be in the \u0026ldquo;low consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.134, \u003cem\u003ez\u003c/em\u003e = -10.201) and \u0026ldquo;local-based foodies\u0026rdquo; profiles (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.091, \u003cem\u003ez\u003c/em\u003e = -6.081).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold Dynamics.\u003c/strong\u003e Compared to households with fewer children, households with more children were significantly more likely to be in the \u0026ldquo;low consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.236, \u003cem\u003ez\u003c/em\u003e = 7.268), \u0026ldquo;local-based foodies\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.238, \u003cem\u003ez\u003c/em\u003e = 6.723) and \u0026ldquo;average consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.484, \u003cem\u003ez\u003c/em\u003e = 12.638). However, households with more children were significantly less likely to be in the \u0026ldquo;high consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -1.000, \u003cem\u003ez\u003c/em\u003e = -9.563) compared to households with fewer children.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to households with fewer teenagers, households with more teenagers were significantly more likely to be in the \u0026ldquo;local-based foodies\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.128, \u003cem\u003ez\u003c/em\u003e = 3.088) and \u0026ldquo;average consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.427, \u003cem\u003ez\u003c/em\u003e = 9.558), whereas households with more teenagers were significantly less likely to be in the \u0026ldquo;high consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.558, \u003cem\u003ez\u003c/em\u003e = -6.750).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to households with fewer adults, households with more adults were significantly more likely to be in the \u0026ldquo;average consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.430, \u003cem\u003ez\u003c/em\u003e = 14.114) and significantly less likely to be in the \u0026ldquo;low consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.113, \u003cem\u003ez\u003c/em\u003e = -4.536), \u0026ldquo;high consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.193, \u003cem\u003ez\u003c/em\u003e = -6.031), \u0026ldquo;nomadic compensators\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.161, \u003cem\u003ez\u003c/em\u003e = -6.953).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Finally, compared to households with fewer elderlies, households with more elderlies were significantly more likely to be in the \u0026ldquo;average consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.486, \u003cem\u003ez\u003c/em\u003e = 9.779) and \u0026ldquo;local-based foodies\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= 0.216, \u003cem\u003ez\u003c/em\u003e = 5.792) and significantly less likely to be in the \u0026ldquo;low consumers\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.198, \u003cem\u003ez\u003c/em\u003e = -5.507) and \u0026ldquo;nomadic compensators\u0026rdquo; (\u003cem\u003eB\u0026nbsp;\u003c/em\u003e= -0.418, \u003cem\u003ez\u003c/em\u003e = -11.851).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[2] Other forms of public transportation (e.g., trams and metros) were not considered because of a data recording error on CBS. Thus, there was no available data for expenses on other public transportation options in 2015. However, the effect may be minimal because the transport monitor by the Netherlands Authority for Consumers and Markets reported over 76% of public transportation trips were made by trains and buses in 2019 (43). Since we would not expect a significant fluctuation in transportation patterns among the Dutch population, using expenditures on trains and buses is a sufficient proxy for expenditures on public transport.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[3] The standard classification groups university graduates from universities of applied sciences and research universities altogether. However, we differentiated between the two because the broader classification results in an overly large category, which includes around 62% of our sample. Furthermore, graduates from the two universities receive different trainings. Whereas research universities offer research-oriented programmes focused on specific subjects, universities of applied sciences offer practical and profession-oriented trainings for students looking to enter a particular career type upon graduation. Therefore, separating these two groups allows us to have a more balanced and meaningful distinction in educational attainment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[4] There is a large proportion of households with no input of education levels because CBS does not have records of elderlies who had completed their education before the registration. Latent Gold performed mean imputations for households that had missing values in education levels (53). As a robustness check, we removed all households with missing values in education levels and ran the analysis. The results remain largely the same, with one exception in the independent variables. More information can be found in Appendix D.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[5] The graph illustrates the relative difference within each indicator rather than absolute monetary values. For actual expenditure, refer to Appendix F. As shown in the table, while the visual representation may suggest a substantial difference in fish expenditures, the absolute difference in dairy expenditures is nearly twice as large. Therefore, the graph should be interpreted only in terms of relative differences within each indicator and not for cross-indicator comparisons.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe current study had two primary goals. The first was to gain new insights into the consumption profiles of the Dutch population by grouping households according to their similarities in household expenditures across five domains. The second goal was to delineate the role of SES components in predicting the probability of belonging to the identified profiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Consumption Profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the first goal, we identified five consumption patterns/profiles: \u003cem\u003eLow consumers, high consumers, nomadic compensators, local-based foodies, and average consumers.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile we provide insights into distinct profiles, interpreting the psychological processes and individual motives underlying these profiles remains speculative. This is because a more detailed understanding of internal processes would require more detailed individual-level data collection.\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;Nomadic compensator\u0026rdquo; (31%) households demonstrated a dissonant consumption pattern that may align with the concept of moral licensing (36), such that households seemingly \u0026ldquo;compensated\u0026rdquo; their high consumption on flights by consuming relatively less in environmentally costly food-domains (e.g., meat and dairy products). Another possibility is that \u0026ldquo;nomadic compensators\u0026rdquo; households have occupations requiring frequent travel. Thus, their dissonant consumption tendency is not a lifestyle choice but an occupational necessity. Both reasons are likely, especially given that results from the multinomial logit indicated that households residing in urbanised regions and relatively highly educated households are more likely to be in the \u0026ldquo;nomadic compensators\u0026rdquo;. \u0026nbsp;Most likely, this profile might even include both types of households and even others.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy contrast, the \u0026ldquo;local-based foodies\u0026rdquo; (18%) shared a different dissonant consumption pattern in which their profile had high environmentally costly food consumption but low travel consumption. This may reflect a preference for investing in food quantity and quality while showing less interest in international or within-country travel. One possibility in explaining their tendencies to consume more locally on food is that households prefer familiarity or that they have long-established community ties to the local environment. An additional possibility is that households in the profile have reduced mobility due to physical constraints or health concerns. Both results are likely, as the results of the multinomial logit indicate that households with more elderlies are more likely to be in \u0026ldquo;local-based foodies\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;average consumer\u0026rdquo; (12%) households demonstrated a consumption pattern that closely aligned with the average of most consumption domains, except for the fact that they had the lowest consumption on residential utilities among the five profiles. The discrepancy could be because \u0026ldquo;average consumers\u0026quot; households may have a larger household size, leading to a lower per-capita consumption on utilities, as suggested by the results of the multinomial logits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe remaining two profiles demonstrated consistently high or low consumption patterns. \u0026ldquo;Low consumers\u0026rdquo; (27%) households demonstrated an overall low consumption on all domains, whereas \u0026ldquo;high consumers\u0026rdquo; (12%) households demonstrated an overall high consumption on all domains. These profiles were strongly related to lower and higher-income households, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 SES on Consumption\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the second goal, we found significant main and interaction effects of education and income in predicting the probability of belonging to different profiles. Having an overall higher education level increases the likelihood of belonging to \u0026ldquo;nomadic compensators\u0026rdquo; and \u0026ldquo;average consumers\u0026rdquo;, while reducing the likelihood of belonging to \u0026ldquo;local-based foodies\u0026rdquo;. Conversely, having higher income decreases the likelihood of belonging to \u0026ldquo;local-based foodies\u0026rdquo;. Interestingly, the positive effect of income on predicting the likelihood of belonging to \u0026ldquo;high consumers\u0026rdquo; is stronger for households of a lower education level, as is the negative effect of income on predicting the likelihood of belonging to \u0026ldquo;low consumers\u0026rdquo;. However, the differences in consumption based on income vanish with higher education. Alternatively interpreted, the higher likelihood of belonging to \u0026ldquo;high consumers\u0026rdquo; with increasing education and the lower likelihood of belonging to \u0026ldquo;low consumers\u0026rdquo; with increasing education is more pronounced for households of a lower income, and the differences in consumption based on education vanish at high-income levels.\u003c/p\u003e\n\u003cp\u003eOur results indicated a nuanced relationship between SES and consumption patterns. \u0026nbsp;Conventionally, individuals with higher levels of education are expected to exhibit more environmentally friendly consumption patterns and lifestyles (e.g., low meat consumption and electricity usage). However, our results suggest that while households with a higher overall level of education tend to consume less in environmentally costly food domains, they exhibited higher consumption in flights and other forms of transportation\u0026mdash;activities that could arguably be even more environmentally costly. Conversely, it is generally expected that individuals with lower levels of education have more environmentally costly consumption patterns (e.g., high meat consumption). While our findings confirm that households with lower education levels do spend more on environmentally costly food products, they tend to have lower travel-related consumption.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, while examining the interaction between income and education in predicting the likelihood of belonging to \u0026ldquo;high consumers\u0026rdquo; or \u0026ldquo;low consumers.\u0026rdquo; A higher income increased the probability of being categorised as a \u0026ldquo;high consumer\u0026rdquo; while reducing the likelihood of being a \u0026ldquo;low consumer.\u0026rdquo; However, this effect was stronger in lower-educated households, suggesting an interdependent relationship between income and education in shaping consumption patterns. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; To our knowledge, the present research is the first to adopt a person-centered approach to understanding environmentally friendly consumption through LPA. We moved beyond the traditional variable-centered approach, which shifted the analytical focus from the behaviours to the consumers. The conceptual change of emphasis allowed us to examine the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emultidimensionality of consumption and inform us of the diversity regarding the consumption profiles in the Dutch population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe person-centered approach also opens several other intriguing questions for future investigations. First, given that consumption preferences are dynamic (56), future research could investigate how consumption profiles evolve over time and how changes in income and education influence shifts in different profiles via a latent transition analysis. Second, future studies could also apply the person-centered approach in different countries or cultural contexts to see if similar profiles emerge or if cultural factors significantly alter the relationships between education and income. Doing so can capture a more comprehensive and dynamic view of consumption that reflects real-world complexity and diversity, in turn, helping us tailor effective policy interventions responsive to cultural and demographic variations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Furthermore, we delineated the role of SES in consumption. The differential effects of SES on consumption profiles corroborate the theoretical supposition of previous literature that education and income may tap into different resources, thus impacting consumption differently. Our study demonstrates the importance of considering income and education as different dimensions of SES in understanding consumption. Future research could build on these findings by investigating the mechanisms underlying this moderation effect, such as environmental concerns and knowledge (8), pro-environmental norms (57), and other possible psychological variables such as self-regulation (58). Additionally, expanding the scope to consider other SES dimensions, such as occupation, wealth, and social capital, would provide a more comprehensive understanding of what drives consumption behaviours. These different directions for future research would build on and strengthen the understanding of the links between SES and consumption. Answers to these questions will greatly inform potential interventions to increase environmentally friendly consumption across all SES groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAltogether, our study highlights the importance of cross-domain policy coordination as promoting sustainable consumption in one domain can cause unintended shifts in another, and these shifts vary depending on their SES. To provide just one suggestion on this, the relation between more extensive flight behaviours and more non-animal related food consumption for higher educated suggests compensation behaviours and, thus, if this group is pushed towards even less animal-related food consumption, they may feel entitled to fly more, which would have a counter-productive overall effect on CO\u003csub\u003e2\u003c/sub\u003e emissions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the encouraging findings, several limitations of our study should be noted. One limitation is the use of a relatively old dataset from 2015. The expenditure survey from Statistics Netherlands were conducted once every five years. Because of travel restrictions, we suspect that consumption, especially in relation to transportation, may differ significantly during and before COVID-19. Therefore, the 2015 cohort is the latest available data source that is most reflective of the typical consumption pattern of the population in the Netherlands. Still, the findings may not fully reflect the current consumption patterns as literature has shown that individuals have reported making modest to significant changes to their behaviours to live sustainably in the past decade (59), and they were actively buying more environmentally friendly products (e.g., sustainable clothing and plant-based milk) than they did five years ago (60). Therefore, future research is needed to validate the consumption profiles using more updated data sources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond, estimating consumption patterns based on household expenditure is limited as expenditure does not necessarily equate to consumption. For instance, the same product but of a higher quality tends to be more expensive than cheaper options. Since higher expenditure automatically equates to more consumption according to our operationalisation, we may overestimate consumption and environmental impacts of wealthier households as wealthier households may tend to purchase higher quality, thus, more expensive goods and services. However, as argued in the carbon emission literature, the environmental impacts and emissions of wealthy households are not necessarily overestimated (60). The reason is that the exact carbon content of more expensive goods and services could not be compared to cheaper ones. For this reason, we may slightly overestimate the environmental impact of wealthier households. Future research can consider other methods, such as measuring the physical units consumed (i.e., quantity-based measures), to further improve the accuracy in estimating consumption patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Furthermore, our sample was not representative of all education levels, and it appeared to over-represent higher education levels, especially given how we handled missing data in education levels via mean imputations. Therefore, the findings of the current study might be applicable to households with higher education backgrounds. Future research is needed to assess generalisability using more representative data that covers households of all education levels. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis study has been approved by the Ethics Committee of the Faculty of Social and Behavioural Sciences of Utrecht University [23-0555]. Participants of the survey data collected by CBS (expenditure data; budgetonderzoek) has given informed consent and were told that their data will be processed confidentially and only used for statistical purposes. For the use of register data (e.g., register data on education;\u0026nbsp;hoogsteopltab), informed consent was not required as the data were collected for official statistical purposes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;No experiments on humans and/or the use of human tissue samples were performed in the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors mentioned in the manuscript have agreed to the authorship, read, and approved the manuscript, and given consent for submission and subsequent publication of the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRitchie H, Rosado P, Roser M. Meat and Dairy Production. 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Available from: https://www.bls.gov/opub/reports/consumer-expenditures/archive/consumer-expenditures-in-2013.pdf\u003c/li\u003e\n\u003cli\u003eBaiocchi G, Minx J, Hubacek K. The Impact of Social Factors and Consumer Behavior on Carbon Dioxide Emissions in the United Kingdom: A Regression Based on Input\u0026minus;Output and Geodemographic Consumer Segmentation Data. J Ind Ecol. 2010 Jan;14(1):50\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eDEFRA. Distributional Impacts of Personal Carbon Trading A research report completed for the Department for Environment, Food and Rural Affairs by the Centre for Sustainable Energy. 2008 Mar. \u003c/li\u003e\n\u003cli\u003eKaza N. Urban form and transportation energy consumption. Energy Policy. 2020 Jan 1;136:111049. \u003c/li\u003e\n\u003cli\u003eYork R, Gossard MH. Cross-national meat and fish consumption: exploring the effects of modernization and ecological context. 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Can J Sociol Cah Can Sociol. 2003 Jan 1;28. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Consumption, Latent Profile Analysis, Socioeconomic Status, Sustainability","lastPublishedDoi":"10.21203/rs.3.rs-7101420/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7101420/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConsumer behaviour significantly contributes to environmental degradation, highlighting the need to understand consumption patterns to inform sustainable policies. Despite growing interest, there is still a lack of nuanced understanding of consumption. First, most studies examined how factors like socioeconomic status (SES) influence isolated consumption behaviours, overlooking the cumulative, cross-domain influence of consumption. Second, while SES is a well-acknowledged and studied predictor of consumption, less is known whether the effects of SES components (income, education) differ and whether they interact. To overcome these gaps, we used microdata from Statistics Netherlands and grouped households based on their fashion, food, transportation, water, and energy expenditures via Latent Profile Analysis. Five subgroups are identified: \u003cem\u003eLow consumers, high consumers, nomadic compensators, local-based foodies, \u003c/em\u003eand\u003cem\u003e average consumers\u003c/em\u003e. Higher-educated households are more likely to be “nomadic compensators” and “average consumers” and less likely to be “local-based foodies”. Higher-income households are less likely to be “local-based foodies”. For other profiles, income and education effects are interdependent. Lower-income or lower-educated households are more likely to be “low consumers”, whereas higher-income or higher-educated households are more likely to be “high consumers”. Among households with lower education levels, higher income increases the probability of belonging to “high consumers”, while lower income increases the probability of belonging to “low consumers”. \u0026nbsp;However, at high-income levels, these differences disappear. Our findings highlight the importance of a person-centered framework in understanding consumption.\u003c/p\u003e","manuscriptTitle":"Five shades of green: Uncovering consumption profile heterogeneity and the role of socioeconomic status in the Netherlands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 12:19:57","doi":"10.21203/rs.3.rs-7101420/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-22T05:38:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-16T15:04:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-11T09:58:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287259965369765908551201370911673825314","date":"2025-08-11T09:37:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178558492495777278000714459941678224321","date":"2025-08-07T17:18:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121797694922261911859458921071600279371","date":"2025-08-06T20:04:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T10:39:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180884821368918535564257665963049541929","date":"2025-07-22T11:06:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-22T10:33:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-14T13:33:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-14T13:32:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-07-11T11:50:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79171e80-2b95-4bc8-92f5-d132b3219afa","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:05:27+00:00","versionOfRecord":{"articleIdentity":"rs-7101420","link":"https://doi.org/10.1007/s43621-026-03186-w","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2026-04-28 15:58:15","publishedOnDateReadable":"April 28th, 2026"},"versionCreatedAt":"2025-07-24 12:19:57","video":"","vorDoi":"10.1007/s43621-026-03186-w","vorDoiUrl":"https://doi.org/10.1007/s43621-026-03186-w","workflowStages":[]},"version":"v1","identity":"rs-7101420","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7101420","identity":"rs-7101420","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}