The relationship between mental health and financial decision-making is increasingly recognised as both complex and consequential. In an ideal economy, access to credit enables individuals to manage day-to-day expenses, invest in long-term goals, and weather financial shocks (World Bank 2022). However, for many, particularly those experiencing psychological distress, this access remains uneven. Mental health conditions such as anxiety and depression can disrupt executive functioning, impair risk assessment, and diminish financial planning capacity, ultimately affecting one’s ability to make informed financial decisions and engage confidently with financial systems.
At the same time, the financial consequences of poor mental health often extend beyond day-to-day budgeting. They can restrict access to essential credit products, hinder asset accumulation, and perpetuate economic insecurity (Capuano & Ramsay 2011). In contrast, short-term credit options such as credit cards and buy-now-pay-later (BNPL) services may appear more accessible, but these instruments often carry higher costs and can entrench financial vulnerability over time. These dynamics are particularly salient in the New Zealand context, where psychological distress has risen markedly over recent years, especially among younger adults, coinciding with high household indebtedness, growing reliance on short-term consumer credit, and persistent barriers to accessing timely mental health support (Ministry of Health 2024; Reserve Bank of New Zealand 2022; Gilbert & Scott 2023).
Despite growing interest in the interplay between mental health and financial outcomes, existing research (for example, Cáceda et al. 2014) tends to focus on broad associations, such as the link between mental distress and overall indebtedness, without distinguishing between types of credit or the underlying mechanisms driving financial exclusion. Questions remain about whether individuals with mental health challenges actively avoid financial commitments such as mortgages or personal loans, or whether systemic barriers such as discriminatory lending practices (Beck & Demirgüç-Kunt 2008) or low financial self-efficacy lead to their exclusion.
This study aims to address these gaps by examining how mental health influences individual debt behaviour across different credit types. Using nationally representative data from the ANZ New Zealand Financial Wellbeing Survey 2021, we investigate whether individuals with poor mental health are more or less likely to hold short-term versus long-term debt and assess the extent to which disparities in credit participation are explained by factors such as financial accessibility, confidence, and social support. We further explore the role of early parental financial guidance as a potential moderating factor in shaping long-term debt engagement.
Theoretically, this study is guided by the financial capability framework proposed by Sherraden (2013), which conceptualises financial behaviour as the outcome of both the ability to act and the opportunity to act. Within this framework, mental health may influence debt participation by constraining individuals’ capacity to engage with financial decision-making and formal credit markets. Evidence shows that mental health problems impair cognitive functioning, planning, and decision quality (for example, Meier & Sprenger 2010; Ridley et al. 2020). They are also associated with higher risks of financial exclusion and reduced access to mainstream financial services (Fitch et al. 2011). Together, these mechanisms provide a conceptual basis for modelling mental health as an antecedent of debt participation, possibly even so in the case of long-term credit, which requires sustained planning and institutional access.
Our empirical analysis uses probit regression to assess the likelihood of holding specific types of debt and applies Oaxaca decomposition to quantify the drivers of observed disparities between individuals with good and poor mental health. The findings reveal that mental health has no statistically significant effect on the likelihood of holding short-term credit such as BNPL and credit cards. In contrast, poor mental health is associated with significantly lower participation in long-term credit, particularly mortgages and personal loans. This exclusion is largely explained by reduced financial accessibility, which accounts for over 77% of the difference in long-term debt ownership. Moreover, we find that individuals with poor mental health are more likely to rely on informal networks such as family and community for financial support, and that early parental financial education can mitigate the adverse effects of poor mental health on long-term borrowing behaviour.
By highlighting the structural and behavioural barriers that limit financial inclusion for individuals with mental health challenges, this paper contributes to the emerging literature at the intersection of financial behaviour and psychological well-being (Ross & Coambs 2018). It also provides important insights for financial planners, policymakers, and institutions seeking to build more inclusive and equitable financial systems. We discuss the policy implications in detail in Section 5.
Recent evidence suggests that the interaction between mental health and debt behaviour is particularly salient in the New Zealand context. The Ministry of Health New Zealand Health Survey show that psychological distress has increased markedly in recent years following the COVID-19 pandemic, especially among younger adults (Ministry of Health 2024). Respondents experienced high or very high psychological distress, which increased from 8.3% in 2019 to 13% in 2024, especially among young adults aged 15–24 years (22.9% in 2024). On the other hand, 10.7% of adults had an unmet need for professional mental health support in 2024, compared to 4.9% in 2017.
At the same time, New Zealand households exhibit high levels of indebtedness, driven primarily by mortgage borrowing. The Reserve Bank of New Zealand (2022) reports that household debt relative to disposable income remains around 170%, placing New Zealand among the most highly leveraged household sectors in the OECD. In parallel, short-term credit products such as credit cards and BNPL services have become increasingly prevalent, particularly among younger consumers and those experiencing income volatility (Gilbert & Scott 2023).
A growing body of research has explored the relationship between mental health and financial behaviour, consistently finding associations between mental health conditions and negative financial outcomes. Studies document positive relationships between depression and financial hardships (for example, Jenkins et al. (2008) find that unsecured debt often relates to poor mental health conditions), and there is clear evidence of a two-directional causation nature of the two subjects (Richardson et al. 2013).
This connection is often explained through cognitive and behavioural pathways. Mental health disorders such as anxiety, depression and emotional distress can disrupt executive functioning processes that control planning and self-regulatory behaviours, leading to impulsive spending, poor planning, lower ability to evaluate financial risks, and increased vulnerability to financial mismanagement (Aldana & Liljenquist 1998; Meier & Sprenger 2010; Lusardi & Tufano 2015; Ross & Coambs 2018; Bai 2023). In turn, poor financial capability imposes psychological burdens that further exacerbate financial despondency or produce financial stress, reinforcing a cycle of financial and emotional distress (Bridges & Disney 2010; Taylor et al. 2011). These mental health challenges also affect broader economic preferences, including attitudes towards work, savings, and consumption (Ridley et al. 2020).
Despite substantial empirical evidence of a correlation between mental health and financial difficulties, the mechanisms underlying this relationship remain contested. Some studies emphasise structural factors, such as low financial literacy and poor access to resources (Lusardi & Mitchell 2014), while others point to behavioural traits, such as impulsivity and low self-control (DeHart et al. 2016). Nevertheless, the question of which factors are most influential in shaping financial outcomes among individuals with mental health challenges remains unresolved.
Short-term debts, such as credit cards, BNPL services, and payday loans, are often used to address immediate financial needs and are typically associated with high interest rates and minimal planning. Due to their reactive nature and costliness, these products can entrap users in debt cycles (Calem & Mester 1995; Obuya 2017), particularly among individuals with a budget plan that is rarely followed (Ajzerle et al. 2015) or with mental health conditions that impair financial self-regulation (Cook et al. 2004). In contrast, long-term debts, such as mortgages and auto loans, are typically used for major life investments and involve structured repayments over extended periods. Owning mortgage debt can serve as informal financial planning (Lee et al. 2007) and promote savings (Bernstein & Koudijs 2024). However, mental health issues such as depression can diminish the capacity to manage these long-term obligations proactively, increasing default risk (Dackehag et al. 2019).
While much of the existing literature establishes a relationship between mental health and overall debt burden, few studies distinguish between short-term and long-term debt types. It limits understanding of how mental health differentially affects debt behaviours depending on the nature and time horizon of financial obligations.
Financial access plays a foundational role in shaping individuals’ borrowing behaviours. Limited access to formal credit often forces individuals to rely on high-cost short-term debt products, thereby perpetuating financial instability (Goodwin et al. 1999). Conversely, access to regulated financial institutions enables more favourable loan conditions and reduces the risk of predatory lending (Casolaro et al. 2006; Karlan & Morduch 2010).
Information channels also influence debt-related decision-making. Martin et al. (2021) find that higher levels of financial literacy are associated with greater control over long-term debt and a reduced likelihood of engaging with exploitative short-term products. Financial literacy programs, professional advisors, and informal networks, which help enhance financial literacy and navigate through the complexity of a debt decision-making process, are an important part of debt management (Garmaise & Moskowitz 2003). Similarly, informal support systems, particularly from family, also enhance financial competence. Guidance received through these channels fosters financial self-efficacy and can improve financial conditions (Kaur & Singh 2024), especially when individuals are denied by formal channels such as professional financial advisers (Johnson 2015). Digital and Artificial Intelligence-based tools have also emerged as accessible alternatives for financial education, especially among younger populations (Pal et al. 2021).
Parental influence during early developmental stages is particularly consequential. Financial attitudes and behaviours modelled in childhood, such as saving habits and responsible borrowing, tend to persist into adulthood (Van Campenhout 2015). Research suggests that parental guidance contributes to more disciplined financial behaviours and reduces the likelihood of future debt-related stress (Shim et al. 2010; Webley & Nyhus 2006). Such early socialisation supports long-term planning and strategic debt decisions, including mortgage management (LeBaron et al. 2021).
Despite the documented benefits of financial access, informational support, and parental guidance, their efficacy in the presence of mental illness remains insufficiently studied. Mental health conditions can hinder the ability to utilise financial literacy and inhibit trust and help-seeking behaviours (Barney et al. 2006). Additionally, the absence of early childhood care and guidance can potentially result in more severe mental problems and undesirable life outcomes (Shonkoff et al. 2012), which could be another factor contributing to reckless financial decisions. On the other hand, Turunen & Hiilamo (2014) suggest that the impact of mental health challenges on financial behaviours can be moderated or intensified by the differences in financial access, financial literacy and social support.
This study is guided by the financial capability framework proposed by Sherraden (2013), which conceptualises financial behaviour as the outcome of the interaction between individuals’ ability to act (financial literacy) and their opportunity to act (financial inclusion). The first building block, financial literacy, encompasses financial socialisation across the life course, as well as knowledge and skills acquired through financial education and advice. The second building block, financial inclusion, refers to the availability and accessibility of financial products and services offered by financial institutions.
Applied to mental health, psychological distress may constrain financial behaviour through both dimensions of financial capability. Mental health challenges can impair planning capacity, confidence, and help-seeking behaviour, thereby weakening individuals’ ability to act. They may also restrict the opportunity to act through interactions with financial institutions, for example, via eligibility requirements or perceived barriers to formal finance. Together, these mechanisms suggest that individuals with poor mental health may face systematic disadvantages in engaging with financial systems, particularly for financial decisions that require sustained planning and institutional interaction.
Consistent with this framework, the empirical analysis is carried out by employing Oaxaca decomposition (which we explain in detail in Section 3.3). Our empirical analysis includes financial accessibility as a measure of financial inclusion, capturing individuals’ opportunity to participate in formal credit markets. Formal financial information channels are treated as institutional mechanisms that facilitate engagement with the financial system, whereas informal information channels, including family and community networks and early parental guidance, reflect financial socialisation processes that shape individuals’ ability to act. Financial confidence is interpreted as a behavioural manifestation of this ability. Accordingly, this study seeks to address gaps in the literature by examining how financial access, informational resources, and early-life parental guidance interact with mental health status in shaping debt participation behaviours in New Zealand.
This study utilises data from the ANZ New Zealand Financial Wellbeing Survey 2021. The survey asked New Zealand participants a wide range of questions regarding their financial situation, health and behaviours (ANZ 2021). The sample includes 1,505 New Zealand Survey participants, with a balanced proportion across different demographics. The final analytic sample has 1,333 observations after excluding respondents with missing values on key variables.
We construct four binary dependent variables derived from survey responses, taking the value of 1 if the respondent holds a specific type of personal debt: BNPL (indicating whether a respondent has instalment payment plans), Credit Card (with or without loyalty rewards), Personal Loan (including car loans), and Mortgage (including residential and investment property), and 0 otherwise.
We also construct additional binary variables to facilitate further analyses. The High Mortgage Debt variable takes the value 1 if the amount of mortgage debt exceeds $50,000, and 0 otherwise. We also define Long-term Debt as holding either a personal loan or a mortgage.
The key independent variable, Poor Mental Health, is a binary measure derived from self-reported responses to the survey question: “How would you rate your overall mental well-being?” Respondents who rate their mental well-being as “Poor” are assigned a value of 1, while all other responses (Fair, Good, Very good, and Excellent) are coded as 0. Our approach is similar to Ahmad et al. (2014).
We include several channel variables to explain the relationship between mental health and personal debt. Financial Accessibility Score is a composite measure, constructed from survey responses to the question “Which of these financial products do you have personally or jointly with someone else?” This variable assesses individuals’ access to a wide range of financial products, such as bank loans, insurance, and investment options (1). Each financial product is represented as a binary variable; the binary values are then summed to produce a total score, which is then normalised to a 0–100 scale, with higher values indicating greater access to financial products.
Formal Channel Score is constructed from a range of binary survey responses to the question “In the past 12 months, which sources have you used for information, guidance or support to help with your finances?” and is expressed as a normalised score on a 0–100 scale. This variable captures the extent to which respondents rely on professional and institutional sources for financial information, such as accountants, financial planners, and government agencies (2).
Informal Channel Score captures the extent to which individuals rely on personal and community-based networks for financial information, guidance, or support. This includes input from friends, family members, community leaders, and informal online resources (3). The variable is constructed by summing binary responses to the survey question: “In the past 12 months, which other sources have you used for information, guidance, or support to help with your finances?” Each selected option is coded as 1, and the total is normalised to a 0–100 scale, with higher scores indicating greater reliance on informal information channels.
Financial Confidence Score is derived from respondents’ self-assessments of their perceived ability to manage everyday finances, plan for the future, and make informed financial decisions. This score is constructed using responses to the following survey questions: “How confident are you in your ability to manage your money day to day?”, “Plan for your financial future?”, and “Make decisions about financial products and services?” Response options include not confident at all, not very confident, neutral, somewhat confident, and very confident. The ordered responses are aggregated and normalised into a 0–100 scale, where higher values indicate greater financial confidence.
Parental Guidance takes a value of 1 if a respondent has significant parental financial guidance and 0 otherwise. It captures the influence of early financial education and support on current financial behaviours. This measure is based on the survey question: “My parents discussed with me how to manage financial matters when I was growing up,” with responses of a five-point Likert scale, where 1 indicates “Does not describe me at all” and 5 indicates “Describes me very well.” Respondents who selected a rating of 4 or 5 are classified as having received substantial parental guidance.
Control variables include demographics such as gender, residential region, education, income, age, ethnicity, and major socio-economic life events that impact financial behaviour, including illness, death of a partner, involuntary job loss and job loss due to caring for families. Detailed categories are presented in Table 1.
Descriptive Statistics
| Variables | Mean | SD | Min | Med | Max |
|---|---|---|---|---|---|
| Debt Ownership | |||||
| Buy Now Pay Later (BNPL) | 0.20 | 0.40 | 0.00 | 0.00 | 1.00 |
| Credit Card | 0.61 | 0.49 | 0.00 | 1.00 | 1.00 |
| Personal Loan | 0.14 | 0.34 | 0.00 | 0.00 | 1.00 |
| Mortgage | 0.33 | 0.47 | 0.00 | 0.00 | 1.00 |
| High Mortgage Debt | 0.28 | 0.45 | 0.00 | 0.00 | 1.00 |
| Long-term Debt | 0.41 | 0.49 | 0.00 | 0.00 | 1.00 |
| Mental Health | |||||
| Good Mental | 0.93 | 0.25 | 0.00 | 1.00 | 1.00 |
| Poor Mental | 0.07 | 0.25 | 0.00 | 0.00 | 1.00 |
| Financial access, information, and confidence | |||||
| Financial Accessibility Score (0–100) | 23.88 | 13.18 | 0.00 | 21.43 | 100.00 |
| Formal Channel Score (0–100) | 11.23 | 13.04 | 0.00 | 11.11 | 100.00 |
| Informal Channel Score (0–100) | 12.99 | 15.07 | 0.00 | 12.50 | 100.00 |
| Financial Confidence Score (0–100) | 74.48 | 20.80 | 0.00 | 75.00 | 100.00 |
| Early Parental Guidance | 0.35 | 0.48 | 0.00 | 0.00 | 1.00 |
| Demographics | |||||
| Gender(%) | |||||
| Male | 0.48 | 0.50 | 0.00 | 0.00 | 1.00 |
| Female | 0.52 | 0.50 | 0.00 | 1.00 | 1.00 |
| Region(%) | |||||
| Auckland | 0.33 | 0.47 | 0.00 | 0.00 | 1.00 |
| Northland | 0.04 | 0.18 | 0.00 | 0.00 | 1.00 |
| Waikato | 0.09 | 0.28 | 0.00 | 0.00 | 1.00 |
| Bay of Plenty | 0.06 | 0.24 | 0.00 | 0.00 | 1.00 |
| Wellington | 0.11 | 0.32 | 0.00 | 0.00 | 1.00 |
| Other North Island | 0.13 | 0.33 | 0.00 | 0.00 | 1.00 |
| Canterbury | 0.13 | 0.34 | 0.00 | 0.00 | 1.00 |
| Otago | 0.05 | 0.21 | 0.00 | 0.00 | 1.00 |
| Other South Island | 0.06 | 0.24 | 0.00 | 0.00 | 1.00 |
| Education(%) | |||||
| Lower Secondary and below | 0.13 | 0.34 | 0.00 | 0.00 | 1.00 |
| Upper Secondary | 0.13 | 0.34 | 0.00 | 0.00 | 1.00 |
| Vocational education | 0.27 | 0.45 | 0.00 | 0.00 | 1.00 |
| Undergraduate and Postgraduate | 0.46 | 0.50 | 0.00 | 0.00 | 1.00 |
| Income(%) | |||||
| Low Income(<$60k) | 0.38 | 0.49 | 0.00 | 0.00 | 1.00 |
| Middle Income($60k–$100k) | 0.29 | 0.45 | 0.00 | 0.00 | 1.00 |
| High Income($100k+) | 0.34 | 0.47 | 0.00 | 0.00 | 1.00 |
| Age(%) | |||||
| 18 – 24 | 0.10 | 0.30 | 0.00 | 0.00 | 1.00 |
| 25 – 34 | 0.19 | 0.40 | 0.00 | 0.00 | 1.00 |
| 35 – 49 | 0.25 | 0.44 | 0.00 | 0.00 | 1.00 |
| 50 – 64 | 0.24 | 0.43 | 0.00 | 0.00 | 1.00 |
| 65+ | 0.20 | 0.40 | 0.00 | 0.00 | 1.00 |
| Race(%) | |||||
| New Zealand European | 0.73 | 0.44 | 0.00 | 1.00 | 1.00 |
| Maori | 0.10 | 0.30 | 0.00 | 0.00 | 1.00 |
| Pacific Peoples | 0.03 | 0.17 | 0.00 | 0.00 | 1.00 |
| Asian | 0.08 | 0.27 | 0.00 | 0.00 | 1.00 |
| Indian | 0.04 | 0.19 | 0.00 | 0.00 | 1.00 |
| Other | 0.02 | 0.14 | 0.00 | 0.00 | 1.00 |
| Socio-economic life events | |||||
| Illness preventing work for 2+ months | 0.08 | 0.27 | 0.00 | 0.00 | 1.00 |
| Death of partner | 0.01 | 0.12 | 0.00 | 0.00 | 1.00 |
| Job loss or redundancy leading to unemployment | 0.09 | 0.28 | 0.00 | 0.00 | 1.00 |
| Left work to care for family member or friend full time | 0.03 | 0.17 | 0.00 | 0.00 | 1.00 |
Table 1 presents the descriptive statistics for the variables used in this study. The debt ownership includes variables such as buy-now-pay-later (BNPL), credit cards, personal loans, and mortgages, as well as high non-mortgage debt, high mortgage debt, and the long-term debt indicator. Mental health highlights the proportion of respondents with good and poor mental health. Financial access, information, and confidence factors capture financial accessibility, usage of formal and informal financial channels, and early parental guidance. Demographic variables, such as gender, age, education, income, and race, provide insights into the sample’s diversity, while socio-economic life event indicators capture significant events such as illness, partner death, job loss, or caregiving responsibilities.
We employ probit regression to examine the association between mental health and various types of debt, as well as the impact of mental health on debt levels, controlling for demographic characteristics
(4) and life events. The probit model estimates the likelihood of an event occurring given the values for the independent variables, assuming that the dependent variable follows a cumulative normal distribution (Breen et al. 2018). The model is specified as:
Where Φ is the cumulative distribution function (CDF) of the standard normal distribution, β0 is the intercept term of the logistic regression function, β1, β2,..., βn are the coefficients associated with each of the predictor variables, and X1, X2,..., Xn are the independent variables that are used to predict the dependent variable. To enhance interpretability, marginal effects are reported to illustrate how a one-unit change in an independent variable affects the probability of the outcome.
Furthermore, we use an Oaxaca decomposition analysis to examine disparities in long-term debt (personal loans or mortgages) between respondents with poor and good mental health, after controlling for demographic and life-event factors. Our explanatory variables include financial accessibility, use of formal and informal channels, and financial confidence. Oaxaca decomposition can help identify the key contributors to outcome disparities, untangle complex relationships, and systematically quantify disparities (O’Donnell et al. cited in Jann 2008a; Jann 2008b). The model is expressed as:
Where D represents the difference in the outcome variable between groups, X1i and X2i are the mean values of the independent variables of the two groups, β1i and β2i are the coefficients for the independent variables in the two groups, β1, β2,..., βn are the coefficients of each of the independent variables, and X1, X2,..., Xn are the independent variables used to predict the dependent variable.
Summary statistics are reported in Table 1. It reveals distinct patterns in credit use. Among respondents, 20% use BNPL services, 61% carry credit card debt, 14% have personal loans, and 33% have a mortgage. 41% of respondents carry long-term debt, either personal loans or mortgages. In terms of debt levels, 36% of respondents report high non-mortgage debt (≥ $5,000), while 28% have high mortgage debt (≥ $50,000). These figures highlight notable differences in debt engagement, both in terms of credit type and debt level. It suggests a greater reliance on short-term borrowing to meet immediate expenses, rather than taking leverage for long-term investments in assets.
Seven per cent of respondents are classified as having poor mental health, while the remaining 93% report relatively stable well-being (5). This distribution is broadly consistent with evidence from the New Zealand Health Survey, which reports that 7.4% of adults experienced high or very high psychological distress in the 2019/20 survey (Ministry of Health 2025). This also underscores the contribution of this paper to the study of the underrepresented minority, who may be financially vulnerable and need support the most. Mental health conditions such as anxiety and depression can impair financial decision-making, planning, and risk assessment, which may lead to suboptimal debt management practices or limit access to long-term financial commitments.
The Financial Accessibility Score averaged 23.88, suggesting limited access to formal financial products such as credit cards, insurance, and investments. Use of formal financial information sources, including accountants, financial planners, and government agencies, is low, with an average score of 11.23. Informal sources, such as family, friends, and community networks, are slightly more utilised, averaging 12.99, indicating a stronger reliance on personal networks for financial advice. The average Financial Confidence Score is 74.48, reflecting generally high self-efficacy in managing finances. Thirty-five per cent report receiving substantial financial guidance from parents during childhood, underscoring the importance of early financial education in shaping long-term financial behaviours.
Gender distribution is relatively balanced, with 52% identifying as female and 48% as male. Most respondents live in Auckland (33%), have an undergraduate or postgraduate degree (46%), report an income less than $60,000 (38%), are aged 35–49 (25%), and are predominantly New Zealand European (73%). Among respondents, 8% report an illness that prevented them from working for over two months, and 9% experienced job loss or redundancy.
In this section, we run model (1) as discussed in Section 3.3 to examine the relationship between mental health and ownership of short-term debt (BNPL and credit cards) and long-term debt (personal loans and mortgages). The results are presented in Table 2.
Probit Regression Results for the Likelihood of Holding Debt
| BNPL | Credit Card | Personal Loan | Mortgage | |
|---|---|---|---|---|
| Poor Mental Health | −0.0176 (0.04) | −0.0583 (0.048) | −0.0686* (0.04) | −0.1202** (0.049) |
| Female | 0.0869*** (0.021) | 0.0093 (0.023) | 0.0209 (0.019) | 0.0157 (0.023) |
| Region (Ref: Auckland) | ||||
| Northland | −0.0062 (0.052) | −0.1420** (0.065) | 0.054 (0.057) | −0.039 (0.059) |
| Waikato | 0.0038 (0.04) | −0.0661 (0.046) | −0.0255 (0.031) | −0.0157 (0.044) |
| Bay of Plenty | 0.0984* (0.052) | −0.0323 (0.049) | 0.0614 (0.042) | −0.0235 (0.05) |
| Wellington | −0.0023 (0.036) | −0.051 (0.042) | 0.021 (0.031) | 0.0358 (0.039) |
| Other North Island | 0.0880** (0.038) | −0.1051*** (0.04) | 0.0703** (0.034) | 0.0623 (0.04) |
| Canterbury | −0.0003 (0.033) | −0.0047 (0.038) | 0.0546* (0.032) | 0.1288*** (0.038) |
| Otago | 0.0082 (0.051) | −0.0525 (0.058) | 0.0319 (0.043) | 0.0577 (0.055) |
| Other South Island | −0.0357 (0.04) | 0.0095 (0.049) | 0.0629 (0.042) | 0.0676 (0.049) |
| Education (Ref: Lower Secondary and below) | ||||
| Upper Secondary | −0.0509 (0.041) | −0.0083 (0.048) | −0.0443 (0.036) | −0.0192 (0.048) |
| Vocational education | 0.0526 (0.038) | 0.0361 (0.04) | 0.0072 (0.033) | 0.0186 (0.041) |
| Undergraduate and Postgraduate | −0.0457 (0.035) | 0.1096*** (0.038) | −0.0107 (0.031) | 0.0217 (0.039) |
| Income (Ref: Low Income) | ||||
| Middle Income($60k–$100k) | 0.0032 (0.027) | 0.1950*** (0.031) | 0.0570** (0.023) | 0.2079*** (0.029) |
| High Income($100k+) | −0.0041 (0.026) | 0.2565*** (0.03) | 0.0679*** (0.023) | 0.3382*** (0.03) |
| Age (Ref: 18–24) | ||||
| 25 – 34 | −0.0013 (0.05) | 0.1564*** (0.05) | 0.021 (0.039) | 0.1401*** (0.043) |
| 35 – 49 | −0.0746 (0.048) | 0.3477*** (0.048) | 0.0827** (0.039) | 0.3305*** (0.043) |
| 50 – 64 | −0.1952*** (0.047) | 0.4123*** (0.049) | −0.0248 (0.037) | 0.2888*** (0.044) |
| 65+ | −0.2908*** (0.045) | 0.5805*** (0.047) | −0.0967*** (0.035) | 0.0157 (0.043) |
| Race (Ref: New Zealand European) | ||||
| Maori | 0.0647* (0.037) | −0.0534 (0.04) | 0.0525 (0.034) | −0.0419 (0.038) |
| Pacific Peoples | 0.0431 (0.061) | −0.1882** (0.086) | 0.0577 (0.063) | −0.1787*** (0.059) |
| Asian | −0.014 (0.038) | 0.0179 (0.047) | −0.0285 (0.031) | −0.1312*** (0.04) |
| Indian | 0.1082* (0.063) | −0.053 (0.069) | −0.0269 (0.045) | −0.1029* (0.061) |
| Other | −0.1489*** (0.046) | −0.2036** (0.091) | −0.0633 (0.053) | −0.0246 (0.093) |
| Illness preventing work for 2+ months | 0.0863** (0.037) | −0.0642 (0.042) | 0.0237 (0.032) | −0.0432 (0.043) |
| Death of partner | 0.0058 (0.088) | 0.0017 (0.107) | 0.084 (0.07) | 0.0507 (0.096) |
| Job loss or redundancy leading to unemployment | 0.0308 (0.035) | −0.0158 (0.045) | 0.0509 (0.032) | −0.0728* (0.042) |
| Left work to care for family members or friends full-time | −0.0723 (0.058) | 0.0576 (0.072) | 0.0231 (0.047) | 0.1241* (0.065) |
| Observations | 1333 | 1333 | 1333 | 1333 |
| McFadden pseudo R2 | 0.1345 | 0.1901 | 0.0836 | 0.2074 |
Table 2 presents the results of probit regression analyses examining the likelihood of respondents holding various forms of debt, including buy-now-pay-later (BNPL), credit cards, personal loans, and mortgages. The independent variables include key factors such as mental health, demographic characteristics, and socio-economic life events. The coefficients represent the marginal effects of each variable on the probability of holding specific types of debt, enabling an interpretation of both the statistical and economic significance of the findings.
Significance levels in the Results are denoted by ***, **, and *, representing the 1%, 5%, and 10% significance levels, respectively.
Results in the first two columns show that poor mental health does not significantly affect the likelihood of using short-term credit products such as BNPL services or credit cards. There may be a countervailing interaction between financial need and access to credit that could account for the lack of statistical significance.
On the one hand, people with poor mental health might be more likely to require short-term credit because they struggle to hold down a job, keep up with their bill payments, or experience unexpected financial emergencies (Worthington 2006; Gilbert & Scott 2023). As a result, impulsive financial behaviours and higher spending tendencies associated with mental health conditions might lead to greater demand for easily accessible credit products, such as BNPL and credit cards (Brown et al. 2005; Fitch et al. 2011).
However, the same individuals often struggle to access credit. Financial institutions often utilise a variety of mechanisms to assess risks, such as credit scores, income data, and employment history, that may disadvantage individuals with mental health challenges (Saunders et al. 2021). Poor mental health may lead to a fluctuating income or prior financial issues that affect a lender’s willingness to lend, particularly for unsecured credit such as credit cards (Harper et al. 2018). In addition, individuals suffering from mental health problems may be less apt to use formal financial institutions or may even avoid credit altogether for fear of being rejected.
The opposing forces of increased demand for credit and limited access to it could cancel each other out, yielding no statistically significant link between mental health and the use of short-term credit in the regression.
In contrast, columns (3) and (4) of Table 2 show that poor mental health substantially reduces the probability of having long-term debt (e.g., personal loans and mortgages). The financial exclusion from long-term debts for respondents with poor mental health is also reinforced by the results presented in Table 3, which find that poor mental health negatively affects the likelihood of having a high level of mortgage debt.
Probit Regression Results for Debt Levels
| High Mortgage Debt | |
|---|---|
| Poor Mental Health | −0.0941* (0.052) |
| Female | 0.0443* (0.025) |
| Region (Ref: Auckland) | |
| Northland | −0.0073 (0.066) |
| Waikato | −0.0473 (0.048) |
| Bay of Plenty | 0.0146 (0.060) |
| Wellington | 0.0174 (0.044) |
| Other North Island | 0.0492 (0.047) |
| Canterbury | 0.1484*** (0.043) |
| Otago | −0.0000 (0.060) |
| Other South Island | 0.1175** (0.058) |
| Education (Ref: Lower Secondary and below) | |
| Upper Secondary | 0.0429 (0.054) |
| Vocational education | 0.0655 (0.047) |
| Undergraduate and Postgraduate | 0.1182*** (0.045) |
| Income (Ref: Low Income) | |
| Middle Income($60k–$100k) | 0.2275*** (0.033) |
| High Income($100k+) | 0.3664*** (0.032) |
| Age (Ref: 18–24) | |
| 25 – 34 | 0.1228*** (0.042) |
| 35 – 49 | 0.3172*** (0.042) |
| 50 – 64 | 0.2326*** (0.042) |
| Race (Ref: New Zealand European) | |
| Maori | −0.0256 (0.042) |
| Pacific Peoples | −0.2102*** (0.064) |
| Asian | −0.1478*** (0.044) |
| Indian | −0.1053 (0.067) |
| Other | −0.1520** (0.076) |
| Illness preventing work for 2+ months | −0.0545 (0.049) |
| Death of partner | 0.0793 (0.120) |
| Job loss or redundancy leading to unemployment | −0.0896* (0.047) |
| Left work to care for family members or friends full-time | 0.1146 (0.070) |
| Observations | 1043 |
| McFadden pseudo R2 | 0.2059 |
Table 3 presents the results of probit regression analyses examining the likelihood of respondents holding high levels of mortgage debt. The independent variables include key factors such as mental health, demographic characteristics, and socio-economic life events. The coefficients represent the marginal effects of each variable on the probability of holding specific types of debt, enabling an interpretation of both the statistical and economic significance of the findings.
Significance levels in the Results are denoted by ***, **, and *, representing the 1%, 5%, and 10% significance levels, respectively.
This means that those struggling with mental health may have systemic barriers that hinder them from taking on long-term financial obligations. Short-term debt, such as credit cards, may be more readily accessible, while long-term debt requires more financial planning, consistent income, and strong creditworthiness—all of which can be adversely affected by mental health conditions. Poor mental health can also be associated with lower income stability, weak financial confidence, or risk aversion, which may lead an individual to naturally self-exclude from long-term options (Thielen 2023; Karlan & Morduch 2010; Banich 2009). On top of this, lenders may apply more stringent eligibility and acceptance measures to borrowers with a history of mental health challenges, effectively excluding them from long-term debt options.
This financial exclusion among individuals with mental health challenges has serious implications. Long-term debt is generally considered healthy debt because it is tied to long-term asset-building or essential investments (Carroll & Cohen-Kristiansen 2021; Blair 2021). Access to long-term credit is a key pathway to building financial security, especially through homeownership or productive borrowing for education or emergencies. Lack of access to such credit limits wealth accumulation, increases housing insecurity, and can lock individuals into cycles of financial stress (Wilkes 1995; Magtulis et al. 2024). Mental health conditions, therefore, not only affect financial behaviours but can also perpetuate structural financial disadvantage across the life course.
Finally, we find that the results on the control variables remain consistent with our expectations. Females are more likely to use BNPL services, while regional disparities indicate that individuals outside Auckland, particularly in the Bay of Plenty, the Rest of the North Island, and Canterbury, exhibit varying levels of debt participation. Higher income and older age are consistently associated with greater access to credit, reflecting financial stability and life-cycle effects. Education shows limited influence, except for degree holders being more likely to own credit cards. Ethnic disparities are notable, with Pacific Peoples, Asians, and Indians significantly less likely to hold mortgages, indicating potential structural barriers. Life events such as illness and job loss also shape debt outcomes, particularly by reducing access to mortgages.
We utilise the Oaxaca decomposition analysis (equation 2) to explain the mechanisms underlying the disparity in long-term debt engagement between individuals with good and poor mental health, via financial accessibility, informational channels, and self-assessed confidence. The results are displayed in Table 4.
Oaxaca Decomposition of Long-Term Debt Disparities by Mental Health Status
| Panel 1: Difference in Estimates | ||
| Coefficient | ||
| Good Mental | 0.4402*** (0.014) | |
| Poor Mental | 0.2500*** (0.051) | |
| Difference | 0.1902*** (0.053) | |
| Panel 2: Explained the difference | ||
| Coefficient | Percentage Explained | |
| Financial Accessibility Score (0–100) | 0.0868*** (0.020) | 77.22% |
| Formal Channel Score (0–100) | 0.0021 (0.003) | 1.94% |
| Informal Channel Score (0–100) | −0.0109* (0.006) | −9.70% |
| Financial Confidence Score (0–100) | −0.0002 (0.001) | −0.18% |
| Total | 0.1124** (0.049) | 69.29% |
| Observations | 1333 | |
Table 4 displays the outcomes of the Oaxaca decomposition analysis, which investigates the differences in long-term debt ownership between those with good mental health and those with poor mental health. Panel 1 highlights the coefficient differences, while Panel 2 presents the decomposition results for various factors, including financial accessibility, usage of formal channels, usage of informal channels, and financial confidence. For each decomposition, the first column indicates the component’s contribution to the long-term debt disparities between the two mental health groups, whereas the second column (%) illustrates the proportion of the gap accounted for.
Significance levels are indicated by ***, **, and *, representing significance levels of 1%, 5%, and 10%, respectively.
Consistent with the probit regression findings in Section 4.3, Panel 1 shows that individuals reporting good mental health exhibit a higher likelihood of holding long-term debt compared to those with poor mental health. The gap is 0.1902 with 1% statistical significance. Next, Panel 2 decomposes the explained portion of this disparity, and our findings are consistent with Turunen and Hiilamo (2014). Financial Accessibility Score accounts for the majority (77.22%) of the observed difference. Individuals with poor mental health may face systemic and psychological barriers that reduce their ability to access formal financial products (Fitch et al. 2011). These may include impaired creditworthiness, institutional bias, or difficulties in navigating complex financial systems. Financial institutions may categorise such individuals as high risk, thereby limiting their access to long-term credit. Furthermore, poor mental health may hinder individuals’ capacity to research, apply for, and negotiate financial products, exacerbating exclusion from formal debt markets.
It is also noteworthy that Informal Channel Score contributes negatively to the explained difference (−9.70%), implying that individuals experiencing psychological distress are more likely to rely on informal networks, thereby offsetting some of the disparity caused by other factors such as financial accessibility. This result underscores the possible channel through which we could narrow the gap in long-term debt between those with good and poor mental health. When formal information channels and financial products are not available, informal networks such as friends and family can positively influence access to healthy credit.
Motivated by the findings on the salience of informal information channels in Section 4.4, we further examine how parental guidance influences the association between mental health and long-term debt (personal loans and mortgages). Kaur & Singh (2024) find that positive financial guidance and support from family have proven effective in enhancing an individual’s overall financial self-efficacy and, in turn, improving financial conditions. Moreover, parental financial education may yield benefits that extend into adulthood and are intergenerational, making it a critical lever for long-term financial well-being (LeBaron et al. 2021; LeBaron & Kelly 2021).
The sample is divided based on the level of reported parental guidance on financial matters during childhood. Results in Table 5 reveal that among individuals who received low levels of parental guidance, poor mental health significantly reduces the likelihood of holding personal loans and mortgages by 10.3% and 15.6%, respectively. In contrast, the effect of poor mental health on long-term debt disappears in the high parental guidance group.
The Role of Parental Guidance
| Low Guidance Group | High Parental Guidance Group | |||
|---|---|---|---|---|
| Personal Loan | Mortgage | Personal Loan | Mortgage | |
| Poor Mental Health | −0.1034** (0.050) | −0.1560*** (0.058) | 0.0126 (0.070) | 0.1129 (0.094) |
| Female | 0.0090 (0.024) | 0.0513* (0.028) | 0.0188 (0.031) | −0.0686* (0.040) |
| Region (Ref: Auckland) | ||||
| Northland | 0.1019 (0.080) | 0.0247 (0.076) | 0.0000 (.) | −0.1839** (0.088) |
| Waikato | −0.0248 (0.042) | 0.0595 (0.054) | −0.0305 (0.041) | −0.1345* (0.074) |
| Bay of Plenty | 0.0694 (0.057) | −0.0705 (0.060) | 0.0885 (0.085) | 0.0608 (0.088) |
| Wellington | −0.0197 (0.039) | −0.0251 (0.047) | 0.0981* (0.055) | 0.1339** (0.068) |
| Other North Island | 0.0235 (0.041) | 0.0590 (0.050) | 0.1606*** (0.062) | 0.0407 (0.071) |
| Canterbury | 0.0086 (0.038) | 0.1026** (0.047) | 0.1184** (0.055) | 0.1619** (0.066) |
| Otago | 0.0449 (0.056) | 0.0306 (0.067) | −0.0206 (0.058) | 0.0960 (0.098) |
| Other South Island | 0.0518 (0.050) | 0.0498 (0.055) | 0.0083 (0.083) | 0.0703 (0.109) |
| Education (Ref: Lower Secondary and below) | ||||
| Upper Secondary | −0.0723 (0.045) | −0.0220 (0.060) | 0.0159 (0.060) | −0.0352 (0.085) |
| Vocational education | −0.0127 (0.041) | −0.0243 (0.050) | 0.0539 (0.056) | 0.1088 (0.076) |
| Undergraduate and Postgraduate | −0.0260 (0.039) | −0.0108 (0.047) | 0.0472 (0.053) | 0.0726 (0.072) |
| Income (Ref: Low Income) | ||||
| Middle Income($60k–$100k) | 0.0560* (0.029) | 0.2312*** (0.035) | 0.0718* (0.038) | 0.1580*** (0.055) |
| High Income($100k+) | 0.0534* (0.029) | 0.3332*** (0.036) | 0.0912** (0.041) | 0.3588*** (0.058) |
| Age (Ref: 18–24) | ||||
| 25 – 34 | 0.0334 (0.049) | 0.1316** (0.055) | 0.0298 (0.068) | 0.2064*** (0.068) |
| 35 – 49 | 0.1163** (0.049) | 0.3405*** (0.054) | 0.0597 (0.069) | 0.3862*** (0.069) |
| 50 – 64 | 0.0179 (0.047) | 0.3090*** (0.055) | −0.0704 (0.063) | 0.2988*** (0.070) |
| 65+ | −0.0695 (0.045) | 0.0190 (0.053) | −0.1228** (0.061) | 0.0763 (0.072) |
| Race (Ref: New Zealand European) | ||||
| Maori | 0.0446 (0.041) | −0.0807* (0.043) | 0.0828 (0.063) | 0.0207 (0.071) |
| Pacific Peoples | 0.1328 (0.096) | −0.1058 (0.084) | 0.0614 (0.095) | 0.0000 (.) |
| Asian | −0.0599 (0.039) | −0.1704*** (0.052) | 0.0178 (0.048) | −0.1305** (0.063) |
| Indian | −0.0969* (0.055) | −0.0908 (0.117) | 0.0118 (0.056) | −0.1542** (0.072) |
| Other | −0.0055 (0.100) | −0.1106 (0.125) | 0.0000 (.) | 0.1156 (0.129) |
| Illness preventing work for 2+ months | −0.0496 (0.044) | −0.0630 (0.052) | 0.1457*** (0.047) | 0.0261 (0.084) |
| Death of partner | −0.0636 (0.106) | 0.0283 (0.146) | 0.1960*** (0.074) | 0.0223 (0.116) |
| Job loss or redundancy leading to unemployment | 0.0694* (0.040) | −0.1030** (0.050) | −0.0233 (0.063) | 0.0249 (0.080) |
| Left work to care for family members or friends full-time | 0.0639 (0.067) | 0.1675* (0.091) | −0.0638 (0.060) | 0.0579 (0.092) |
| Observations | 852 | 852 | 428 | 437 |
| McFadden pseudo R2 | 0.0889 | 0.2323 | 0.179 | 0.2262 |
Table 5 presents the subsample analysis results exploring the differential impact of mental health on long-term debt outcomes, specifically personal loans and mortgages, across two groups: individuals with parental financial guidance and those without. The analysis provides separate coefficients for each debt type, comparing the effects of poor mental health, demographic characteristics, and socio-economic factors within these subgroups.
Significance levels are indicated by ***, **, and *, representing significance levels of 1%, 5%, and 10%, respectively.
These findings suggest that early financial education can buffer the negative impact of mental health challenges on financial inclusion. Parental guidance appears to provide a foundational form of financial socialisation, equipping children with essential skills like budgeting, saving, and understanding credit (Serido & Deenanath 2016). These early lessons can improve later financial decision-making, even for those with psychological vulnerabilities. Moreover, parental involvement may foster familiarity and confidence in navigating financial institutions, reducing psychological and institutional barriers to credit (Kortesalmi et al. 2024). This trust can encourage proactive behaviours, such as seeking information, comparing financial products, and negotiating with lenders.
To address concerns regarding model specification, measurement, and functional form, we conduct a series of robustness checks.
First, we reestimate the main model using alternative debt classifications to examine whether the results are sensitive to the definition of debt. Specifically, we construct two additional binary variables to distinguish between short- and long-term debt. Short-term debt is defined as buy-now-pay-later or credit card debt, while long-term debt includes personal loans or mortgage debt. This distinction allows us to assess whether mental health relates differently to debt instruments with varying maturities and commitment horizons. Second, to assess the robustness of our results to the choice of link function, we reestimate the alternative debt classification models using logit rather than probit specifications. As both models rely on different distributional assumptions, consistency between them strengthens confidence that our results are not driven by functional-form choices. The logit results, reported in Panel A of Table 6, closely mirror the probit estimates. The results indicate that mental health is not significantly associated with short-term debt participation but is negatively related to long-term debt, consistent with our baseline findings.
Robustness Checks
| Panel A: Alternative Debt Classifications (Logit) | ||||
| Short-term Debt | Long-term Debt | |||
| Poor Mental Health | −0.0312** (0.043) | −0.1501* (0.051) | ||
| Controls | YES | YES | ||
| Observations | 1333 | 1333 | ||
| McFadden pseudo R2 | 0.1101 | 0.2035 | ||
| Panel B: Alternative Models – Excluding Age (Probit) | ||||
| BNPL | Credit Card | Personal Loan | Mortgage | |
| Poor Mental Health | −0.0132** (0.024) | −0.0582** (0.041) | −0.0612** (0.022) | −0.1051** (0.020) |
| Controls | YES | YES | YES | YES |
| Observations | 1333 | 1333 | 1333 | 1333 |
| McFadden pseudo R2 | 0.1245 | 0.1843 | 0.0784 | 0.1999 |
| Panel C: Alternative Models – Excluding Income (Probit) | ||||
| BNPL | Credit Card | Personal Loan | Mortgage | |
| Poor Mental Health | −0.0169** (0.024) | −0.1002* (0.069) | −0.0759*** (0.008) | −0.1815* (0.093) |
| Controls | YES | YES | YES | YES |
| Observations | 1333 | 1333 | 1333 | 1333 |
| McFadden pseudo R2 | 0.1244 | 0.1411 | 0.0698 | 0.1264 |
Table 6 presents the robustness check results. In Panel A, we reestimate the main model using alternative debt classifications and logit. Short-term debt is defined as buy-now-pay-later or credit card debt, while long-term debt includes personal loans or mortgage debt. In Panels B and C, we reestimate the probit models by excluding age and income controls separately.
Significance levels are indicated by ***, **, and *, representing significance levels of 1%, 5%, and 10%, respectively.
Third, to examine the sensitivity of our findings to potential measurement error or outliers, we reestimate the probit models by excluding age and income controls separately. These variables capture standard life cycle and socio-economic effects but may also be correlated with both mental health and financial access. Excluding them provides a specification-based robustness check. As shown in Panels B and C of Table 6, the estimated effects of mental health remain qualitatively unchanged in terms of sign, statistical significance, and economic magnitude, indicating that the results are not driven by the inclusion of specific demographic or income controls.
Fourth, to explore potential nonlinearity in the relationship between mental health and debt participation, we reclassify mental health into three broader categories: (1) poor or fair, (2) good, and (3) very good. Under this alternative categorisation, the relationship between mental health and debt choices is no longer statistically significant. This finding suggests that the effect of mental health on debt participation is nonlinear and sensitive to how mental health is measured, reinforcing the importance of modelling mental health at a finer level of granularity. To save space, we do not present the results; the results are available upon request.
Finally, although comparable New Zealand data from other survey years are not currently available to facilitate a temporal validation, our main findings are consistent with prior qualitative evidence from New Zealand (Peace et al. 2002) and recent survey-based evidence from the United Kingdom (D’Arcy & Undy 2025), lending external support to our empirical results.
This study sheds new light on the complex relationship between mental health and debt behaviour, using nationally representative data from the ANZ New Zealand Financial Wellbeing Survey 2021. We find a clear pattern: while mental health does not significantly affect the likelihood of holding short-term debt, such as credit cards or BNPL services, it significantly reduces the likelihood of engaging with long-term financial products, such as personal loans and mortgages. This disparity is largely explained by financial accessibility, which accounts for over three-quarters of the difference in long-term debt ownership between individuals with poor and good mental health. Notably, reliance on informal networks such as friends and family partially offsets this gap. Furthermore, early parental financial guidance emerges as a crucial protective factor, neutralising the negative effects of poor mental health on long-term debt participation.
These findings reflect the interaction between rising psychological distress, high household leverage, and institutional features of credit provision in New Zealand. In this setting, the finding that poor mental health is associated with exclusion from long-term credit, but not short-term borrowing, suggests that financial vulnerability is driven less by excessive risk-taking than by structural barriers to productive credit participation. The Oaxaca decomposition results reinforce the central role of financial inclusion in enhancing financial participation and, eventually, financial well-being, consistent with the financial capability framework proposed by Sherraden (2013). Long-term credit requires sustained planning capacity, income stability, and effective navigation of formal institutions, all of which may be disproportionately difficult for individuals experiencing psychological distress. These dynamics are further reflected in New Zealand’s reliance on family and community networks, where formal financial engagement remains limited.
Our findings carry important implications for financial planning professionals and institutions aiming to serve a diverse and inclusive client base:
Expanding the Role of Financial Planners as Inclusive Facilitators
Financial planners and advisers must recognise mental health as a structural determinant of financial behaviour. Clients with mental health challenges may avoid engaging with long-term financial commitments, not due to a lack of interest or understanding, but because of reduced financial self-efficacy and financial systems that are often designed in ways that fail to accommodate mental health symptoms, thereby limiting engagement with mainstream financial services (Cook et al. 2004; Lown 2011; Smith & Stacey 2025). Planners can act as facilitators by offering trauma-informed and judgement-free advice, building trust over time, and adopting client-centred planning approaches. Incorporating mental health sensitivity into client onboarding and fact-finding processes can also reduce barriers to disclosure and engagement (Lawson & Klontz 2017; Cook et al. 2004).
Improving Access to Long-Term Financial Planning Tools
Because long-term debt instruments like mortgages and personal loans are often gateways to wealth building, the exclusion of individuals with poor mental health from these products risks entrenching financial inequality. Financial planners, working with lenders and policymakers, should advocate for alternative assessment methods that go beyond traditional credit scoring. Additionally, advisers can promote the use of budgeting tools, amortisation modelling, and staged goal setting to help clients experiencing psychological distress envision and work toward long-term financial goals.
Strengthening Financial Socialisation and Parental Guidance
Given the robust buffering effect of early parental financial guidance, policymakers and educators should invest in programmes that support family-based financial socialisation. Schools can embed financial education in the curriculum from an early age, while community initiatives can equip parents with tools and resources to model and teach sound financial behaviours. Communities and workplaces are also uniquely positioned to foster resilience through informal networks and workplace-based interventions. Government-backed campaigns might also promote intergenerational financial literacy by incentivising family, workplace and community conversations around budgeting, saving, and debt management.
Collaborating Across Sectors to Build an Inclusive Ecosystem
Policymakers, financial advice providers, and community organisations should collaborate to develop an ecosystem where mental health and financial wellbeing are jointly supported. Public-private partnerships could fund integrated service models where financial planners work alongside mental health practitioners. Financial advice regulations may also evolve to include mental health considerations within the fiduciary duty of care.
To summarise, promoting equitable access to long-term financial advice and planning requires more than technical competence. It requires empathy, cultural humility, and systemic awareness. By embracing mental health as a core dimension of inclusive practice, financial planners and policymakers alike can play a transformative role in building financial wellbeing for all.
Finally, we acknowledge several limitations of this study and outline avenues for future research. First, while this study focuses on debt participation and financial accessibility rather than debt-induced distress, we recognise that the relationship between mental health and financial outcomes may be bidirectional. Future research could more explicitly model this reciprocal relationship and examine the dynamic interplay between mental health and debt over time. Second, potential endogeneity remains a valid concern given this bidirectional relationship. However, our ability to address this issue is constrained by the cross-sectional nature of the data. Future studies employing longitudinal or experimental designs would be well positioned to identify causal pathways between mental health and debt behaviour and to more effectively disentangle endogeneity concerns.
Third, our measure of mental health is based on a single self-reported assessment of overall mental well-being, which necessarily provides a simplified representation of a complex psychological construct. While single-item self-rated mental health measures are widely used in large-scale population surveys (e.g., Ahmad et al. 2014) and have been shown to correlate meaningfully with broader indicators of psychological well-being, they cannot capture the multidimensional nature of mental health. Future research could build on this study by utilising validated multi-item instruments, such as the Kessler Psychological Distress Scale (K10), PHQ-9, or the SF-12 Mental Component Summary, when such data become available. The use of richer mental health measures would allow for a more nuanced examination of how different dimensions and severities of psychological distress relate to debt participation and financial inclusion.
Finally, a promising direction for future research would be to examine whether different forms of debt, such as short-term versus long-term debt, mediate the relationship between mental health and overall financial well-being. Investigating these potential mediating pathways could provide deeper insights into the behavioural mechanisms through which psychological functioning influences financial decision-making and outcomes. While such mediation analysis lies beyond the scope of the present study, it represents an important theoretical and methodological extension of our framework.
Response options include payday loan, BNPL/instalment plan, loan from friend/family, an investment property that is not financed by a loan/mortgage, investment or margin loan, managed fund, share portfolio, KiwiSaver, home and contents insurance, rental or landlord insurance, car insurance, life Insurance/total permanent disability insurance, income protection/unemployment cover, cryptocurrency, and other.
Response options include an accountant, a bank employee, a bank website, a taxation specialist, a budget advisor/financial mentor, a financial planner or an advisor, the Citizens Advice Bureau, a financial product rating agency, a government body like the Commission for Financial Capability (now renamed Te Ara Ahunga Ora Retirement Commission), and other.
Response options include close relatives, close friends, church pastor/minister or community leader, community organisation/not-for-profit, books on managing your finances and investment, employer, other website, blog, webinar, podcast or other online resource, and other.
When including categorical demographic variables, reference categories are chosen based on sample distribution. For example, New Zealand European respondents are the largest ethnic group in the sample (73%) and are used as the reference category for comparisons and interpretations.
The distribution of self-reported mental health is 19% “Fair”, 34% “Good”, 26% “Very good”, and 14% “Excellent”.