Self-control and debt decisions relationship: evidence for different credit options

Vol.:(0123456789) 1 3 Current Psychology https://doi.org/10.1007/s12144-023-04251-7 Self‑control anddebt decisions relationship: evidence fordifferent credit options SaraFernández‑López1 · SandraCastro‑González2 · LucíaRey‑Ares1 · DavidRodeiro‑Pazos1 Accepted: 6 January 2023 © The Author(s) 2023 Abstract In recent years, households have become increasingly indebted, which constitutes a matter of concern. Based on a sample from the Spanish Survey of Financial Competences, this paper examines the relationship between self-control and consumer borrowing behaviour and, in so doing, it proposes a new multi-item scale of individual’s self-control in financial matters and considers several types of consumer debt, thus overcoming the main gaps identified in previous research. Empirical evidence reveals that, overall, self-control problems lead to more indebtedness. However, comprehensive analyses have demonstrated that the influence of self-control differs across an exhaustive range of credit options. In this regard, the lack of self-control increases the probability of taking out unsecured personal loans, loans from family or friends, and credit card use. Evidence suggests that individuals’ first impulse leads them to ask their social circle for a loan. Overall, individuals might be tempted to get indebted when they cannot make ends meet. However, this first impulse usually faces barriers from the supply perspective, so the evidence suggests that the effect of self-control on borrowing decisions might be shaped not only by the demand side of credit but also by the supply one. Keywords Household indebtedness· Consumer debt· Behavioural finance· Exploratory Factor Analysis· Self-control· Credit JEL classification codes G41· G51 Introduction Most developed economies have experienced a sharp increase in household debt, also known as the ‘democratisation of credit’ (Kukk, 2019), with continually rising insolvency levels over the past two decades (König & Größl, 2014). This unprecedented rise in household debt not only increases the financial vulnerability of households but also makes the occurrence of financial crunches more likely (Faria etal., 2012). The current situation is not particularly encouraging due to the growing levels of indebtedness of European households after the Great Recession of the 21st century (Angel & Heitzmann, 2015). Thus, in 2017, household debt represented 110.8% of household net disposable income in Spain, reaching 246.2% in the Netherlands (OECD, 2019). Household borrowing is a complex phenomenon influenced by several processes. In this regard, previous studies have acknowledged the effect of psychological and attitudinal traits in explaining individuals’ borrowing behaviour (Achtziger etal., 2015). Among these traits, self-control has emerged as one of the driving forces behind financial decisions (Gathergood, 2012) and, particularly, household debt. An emerging stream of the literature in this area has found evidence that a lack of self-control increases debt-holding by * David Rodeiro-Pazos da[email protected] Sara Fernández-López sara.f[email protected] Sandra Castro-González [email protected] Lucía Rey-Ares lucia.rey[email protected] 1 Department ofFinancial Economics andAccounting, Universidade de Santiago de Compostela, SantiagodeCompostela, Galicia, Spain 2 Department ofBusiness Organization andCommercialisation, Universidade de Santiago de Compostela, Lugo, Galicia, Spain Current Psychology 1 3 encouraging individuals with time inconsistency problems to make impulsive decisions concerning money. Hence, the literature’s understanding of why household debt has sharply increased in recent years is essential for sound policymaking (Faria etal., 2012). To date, research connecting household indebtedness to self-control is still underdeveloped. Only a few studies (Achtziger etal., 2015; Gathergood, 2012; Gathergood & Weber, 2014; Wang etal., 2011; Webley & Nyhus, 2001; Wilcox etal., 2011) have explicitly addressed this issue. Two major limitations exist in previous research. Firstly, the extant studies analyse either a specific type of debt or an aggregate measure of household debt. However, no study explores the influence of self-control on several types of household debt. Secondly, the self-control measures adopted in those studies suffer from specific weaknesses, such as the use of measurement scales based on a limited number of items (e.g., Gathergood & Weber, 2014); or based on general behaviours, such as smoking or drinking, rather than on financial choices (e.g., Achtziger etal., 2015; Wang etal., 2011). Moreover, most of the studies on this topic overlook the role played by financial literacy in household debt, even though a strand of the literature has acknowledged that low levels of financial literacy are associated with a costly use of debt (Lusardi & Tufano, 2009). Nonetheless, this study goes beyond previous research. Firstly, our evidence identifies that a lack of self-control causes individuals to become more indebted and that the impact of self-control on borrowing behaviour is greater among those individuals displaying the lowest levels of self-control. Secondly, empirical evidence confirms that the effect of self-control differs depending on the type of debt. These differences seem to rely more heavily on the borrowing constraints of the supply side of credit than on the demand side (i.e., the individual). This study aims to address the gaps mentioned above. Drawing mainly on the behavioural life cycle (BLC) hypothesis, it analyses whether individuals’ self-control in financial matters affects their borrowing behaviour across different types of debts. To this end, in the first stage, the authors design and validate a multi-item scale of financial self-control; the scale is validated using Exploratory Factorial Analysis (EFA). In a recent study, Nilsen etal. (2020) acknowledge that most existing measures of self-control are limited, as they fail to consider the multidimensionality and specificity of the trait or situation to which they are applied. Besides, these measures are mostly designed to measure a general trait of self-control instead of the concept as a whole (Lindner etal., 2015). The development of this new scale facilitates the more efficient measurement and prediction of results as the statements on self-control refer to individual behaviours regarding financial choices rather than general behaviours. In the second stage, the authors consider up to nine types of household and individual debts and explore whether the effect of self-control differs across them. Moreover, all the estimated models include financial literacy as a control variable. This study contributes to the literature in the following ways. Firstly, it extends the existing theoretical literature on self-control and household debt by demonstrating that the effect of self-control varies depending on the type of debt. Secondly, the study proposes a new multi-item measure of self-control within the financial domain. This study responds to the claim that a more financial-oriented self-control measure is needed when examining financial matters (Lown, 2011). Thirdly, it also complements previous empirical evidence since the analysis relies on a much broader sample than previous studies, namely one consisting of 8,554 Spanish individuals, which allows the study to not only obtain more robust and generalisable results (Farrell etal., 2016) but also control for a rich set of household and individual characteristics such as financial literacy. Finally, our evidence contributes to the empirical literature on consumer finance by demonstrating that individuals’ behavioural characteristics (and particularly, self-control) have significant impacts on individuals’ borrowing decisions, thus contributing to knowing the driving forces of indebtedness decisions and allowing to make meaningful recommendations aimed at improving household debt behaviours and preventing over-indebtedness. The paper is structured as follows: Sect."Literature review" addresses the literature review; Sect."Data" describes the data; Sect."Econometric model and estimation" presents and discusses the findings resulting from the multivariate analysis; and Sect."Discussion" ends by summarising the concluding remarks. Literature review The construct of self-control has been conceptualised differently, but regardless of its conceptualisation, there is consensus on how it is defined (Mansfield etal., 2003). Self-control can be defined as individuals’ ability or capacity to control their own states, responses, emotions, or impulses to protect a valued goal or resist a temptation (Gerhard etal., 2018). Individuals’ capacity for self-control can vary and thus affect their future financial well-being, as those with self-control problems are less willing to save for the future and more willing to spend in the present (Mpaata etal., 2021; Thaler & Benartzi, 2004). Self-control has often been considered an intrapersonal decision time-inconsistency problem Current Psychology 1 3 (Gathergood, 2012; Zhao etal., 2021) by the behavioural finance approach. Accordingly, it is generally accepted that a lack of self-control leads individuals to fall into their first or dominant impulses, namely, the inability to resist temptations or cope with their emotions. The dilemma of self-control is between choosing the here and now or postponing something to an uncertain future, that is, dealing with the trade-off between instant rewards and long-term interests, hence its connection with the BLC hypothesis. Indeed, as Strömbäck etal. (2017) point out, according to the above-mentioned theoretical hypothesis, individuals behave as if within each person, there is an ongoing conflict between a “planner” who is more concerned about the long term and a “doer” who is more concerned with the present time. According to this, individuals are torn between worrying about getting things done now or planning for the long term, assuming the temptation to spend is greatest for current income and lowest for future income (Shefrin & Thaler, 1988). Thus, these authors incorporate self-control in the BLC hypothesis, considering self-control as a prospective antecedent of households’ saving and consumption decisions. In this perspective, individuals face an internal conflict and might not be rational in their decisions. Here, selfcontrol emerges as a driving force aimed at controlling individuals’ first impulses regarding consumption behaviour to foster savings. Thus, the ability to control impulses and emotions can influence individuals’ decision-making (Atkinson & Messy, 2011), enabling them to manage their finances successfully (Farrell etal., 2016). Whilst extant theory has acknowledged the importance of self-control as a significant driver of financial behaviour since the 1980s (Shefrin & Thaler, 1988), the empirical literature on the issue remains scarce. This could partly be because, until recently, there has been a lack of multidisciplinary datasets (i.e., datasets gathering information on both psychological traits – such as self-control – and financial decisions). Hence, only recently have a number of studies analysed in more detail the role of self-control in financial decisions. These studies have emphasised two main specific financial behaviours: household (e.g., Liu etal., 2019; Thaler & Benartzi, 2004) and retirement savings (e.g., Ameriks etal., 2007; Hira etal., 2009; Kimball & Shumway, 2009); and household debt (e.g., Bu etal., 2022; Gathergood, 2012; Wang etal., 2011). This section continues to present the most relevant contributions in the field, thus motivating the gaps in the literature that are tackled throughout this paper. To the best of the authors’ knowledge, Webley and Nyhus (2001), Wang etal. (2011), Gathergood (2012), Gathergood and Weber (2014), and Achtziger etal. (2015) are the only studies that have explicitly addressed the relationship between self-control and household debts (Table1). Thus, Webley and Nyhus (2001) find that a lack of selfcontrol causes individuals to get into more debt. Their empirical evidence is based on a sample of 4,147 Dutch individuals. Self-control is measured through three indexes: two are related to health and healthy habits, and one is focused on spending styles. In turn, debt is examined through an aggregate measure that combines information regarding arrears, bank debt, the extent of credit, self-reported financial situation, perceived creditworthiness, and the stated relationship between income and expenditure. Wang etal. (2011) investigate a sample of 837 Chinese credit card holders. Attention is paid to the effect of selfcontrol on credit card usage, finding that self-control is negatively associated with revolving credit use. Those authors employ a multi-item psychological scale to measure selfcontrol and consider it a personality factor. Using the same self-control scale and a sample of 946 German individuals, Achtziger etal. (2015) conclude that self-control negatively affects consumers’ current debts, measured through an aggregate index of private loans, general bank loans, and other debts. Furthermore, Wilcox etal. (2011), based on five experimental and field studies, find evidence that consumers with high self-control increase spending when the available credit on their credit card is low. However, consumers decrease spending when the available credit is high. In so doing, they use a general measure of self-control based on a 13-item scale capturing individuals’ overall tendency to exercise restraint in different domains. Gathergood (2012) identifies that a lack of self-control increases household over-indebtedness in a sample of 1,234 United Kingdom (UK) households, increasing their financial vulnerability and exposition to financial shocks. Namely, this author confirms that those individuals with self-control problems make greater use of quick-access but high-cost credit items such as payday loans. To measure the level of self-control, Gathergood (2012) employs a single statement aimed at discovering whether the respondents act impulsively regarding financial choices. Using the same statement and based on a larger sample comprised of 2,584 UK households, Gathergood and Weber (2014) find that low levels of self-control increase the co-holding (i.e., the simultaneous holding of consumer credit and liquid assets) and consumer borrowing. It is noteworthy that, unlike previous studies, Gathergood (2012) and Gathergood and Weber (2014) consider the influence of financial literacy on household borrowing. This represents a significant contribution, given that plenty of studies have proven that individuals’ financial literacy conditions their debt status (Agarwal etal., 2010; Collard etal., 2012; Disney & Gathergood, 2013; French & McKillop, 2016; Lusardi & Tufano, 2009; Servon & Kaestner, 2008; Sevim etal., 2012). Similarly, Meier and Sprenger (2010) concluded, after a field study, that present-biased individuals are more likely to Current Psychology 1 3 Table 1 Studies on self-control and household debts Authors Sample Year of the sample Dependent variable: debts Key independent variable: selfcontrol Financial literacy as a control variable Webley and Nyhus (2001) 4,147 Dutch individuals (households’ heads, together with their spouses or partners) 1994, 1995, 1996 An aggregate measure that combines information on (HOMALS analysis): - Arrears - Bank debt - Extent of credit - Self-reported financial situation - Perceived credit worthiness - The stated relationship between income and expenditure Three indexes: -First index. Spending styles (from ‘I like to spend all my money immediately’ to ‘I want to save as much as possible’) -Second index composed by two items: weight and height -Third index composed by three questions on smoking and drinking No Wang etal. (2011) 837 Chinese individuals (credit card holders) 2008 Two specific behaviors: - Revolving credit use (one index through the average of four items) - Petty instalment credit use (one index through the average of four items) Eleven self-reported items based on a previous scale No Gathergood (2012) 1,234 UK households 2010 Three measures of over-indebtedness: - Self-reported measure of overindebtedness through three items - One-month delinquency on at least one credit item - Three-month delinquency on at least one credit item Three self-reported items Yes. Three questions on: - Simple interest - Compounding interest - Monthly payments Gathergood and Weber (2014) 2,584 UK households 2010 One question regarding co-holding A single item which describes impulsive behavior (i.e. the lack of self-control) Yes. Three questions on: - Simple interest - Compounding interest - Minimum payments Achtziger etal. (2015) 946 German individuals - An aggregate measure resulting from the consideration of the amounts of: - Private loans - General bank loans - Other debts An index resulting from a sum of the scores of a scale of eleven self-reported items No Current Psychology 1 3 borrow and, conditionally, to have a more significant amount of credit card debt. In short, the aforementioned studies acknowledge that self-control impacts household debt. Along this line of thinking, our first hypothesis is proposed: Hypothesis A: The lower the individuals’ level of selfcontrol, the greater their probability of getting into debt. Nevertheless, as mentioned above, two major gaps exist in the research examining the relationship between self-control and borrowing behaviour. Firstly, previous studies have failed to distinguish between different types of debt. Thus, Wang etal. (2011) and Wilcox etal. (2011) both considered exclusively credit card debt, and Gathergood (2012) focused on over-indebtedness. In contrast, Webley and Nyhus (2001), Gathergood and Weber (2014), and Achtziger etal. (2015) aggregated different measures of debt into a single one. This paper goes beyond these studies, proposing that the effect of self-control may differ according to the type of debt, as each has its specific features. Although there is no evidence regarding self-control, previous studies have shown that attitudinal factors have a different influence on the use of money or the debt incurred. Namely, Wang etal. (2011) ascertained that attitudes and, more specifically, dimensions of attitudes differently influence individuals’ behaviour towards credit cards or other types of debt. Loibl etal. (2021) show that the type of attitude towards debt also influences how individuals behave towards the use or non-use of revolving credit. The underlying argument here is that different types of debt involve different levels of difficulty in both access to credit and repayment conditions. Thus, the first impulse of an individual with self-control problems might be to borrow from family and friends because it is expected that they would offer better repayment conditions than those offered by the financial sector (i.e., lower or zero interest rates and flexible repayment schedules). However, when the individual does not have this choice, the alternative may be to take out a bank loan to make ends meet. In such cases, they are conscious that this choice is subjected to financial scrutiny and risk assessment by bank officers. Hence, this first impulse can be re-directed to ‘easier’ ways of indebtedness, such as using existing credits or borrowing from employers. Hypothesis B: The relationship between individuals’ selfcontrol and borrowing behaviour will depend on the type of debt considered (i.e. individual vs household debt). Secondly, there is a lack of agreement regarding measuring self-control. In most studies, self-control refers to general behaviours (Achtziger etal., 2015; Wang etal., 2011; Webley & Nyhus, 2001) rather than to financial matters. Only Gathergood (2012) and Gathergood and Weber (2014) constructed a measure relying on individual behaviour regarding financial choices. In addition, these authors consider financial literacy as a control variable. Data Survey andsample characteristics The data used in this study comes from the Survey of Financial Competences (Banco de España and CNMV, 2018), a questionnaire-based survey integrated into the ‘Measuring Financial Literacy and Financial Inclusion’ initiative of the OECD’s International Network of Financial Education (INFE). The analysis is based on a dataset comprised of 8,554 Spanish individuals between 18 and 79years of age. Data collection occurred between the fourth quarter of 2016 and the second quarter of 2017. The survey assesses the financial knowledge, behaviours and attitudes of a representative sample of the Spanish population, gathering information on its personal and demographic characteristics. This information allows the study to use a rich set of demographic and socio-economic characteristics often highlighted by the literature as driving forces of household borrowing. Table2 describes the set of independent variables used. Another advantage of using the Survey of Financial Competences is that it contains detailed information about individuals’ financial competences. Indeed, this is the only survey that allows its assessment of the Spanish population. As mentioned previously, apart from Gathergood (2012) and Gathergood and Weber (2014), none of the extant studies have paid attention to this potential driver. Table3 summarises the socio-economic and demographic characteristics of the sample. Evidence shows that 50.2% of respondents are women and the average age of the sample is close to 47years. Additionally, 65.7% of respondents are married, and 30.7% have children younger than 18years living at home. Regarding educational attainment, 27.2% completed the first stage of secondary education, 22.8% the second stage of secondary education, and 22.3% undertook university studies. Regarding employment status and economic situation, 53.1% of the respondents are employed or self-employed, and almost half of the sample (50.5%) has a yearly household gross income between 14,500 and 45,000 euros. Finally, the three-item measure of respondents’ financial literacy is constructed based on the ‘core’ of financial literacy and stems from three questions developed by Lusardi and Mitchell (2011a). These questions refer to the concepts of interest compounding, real and nominal returns, and portfolio diversification. Hence, the financial literacy measure consists of four levels, which reflect the number of questions a respondent can answer correctly, ranging from 0 if Current Psychology 1 3 all answers are wrong to 3 if all three answers are correct. Thus, 13.3% of respondents failed to correctly answer any of the three questions proposed by Lusardi and Mitchell (2011b), and only 18.9% answered all questions correctly. Most respondents can correctly answer one or two of the three questions on financial literacy. Measures ofhousehold debts Unlike the studies summarised in Table1, this paper measures household borrowing behaviour through different types of consumer debt. The Survey of Financial Competences contains detailed data on household consumer debts throughout its different sections, leading to the definition of up to nine dependent variables, that are described in Table4 and subsequently considered in the empirical analyses. Thus, in a specific section related to the financial products held by households, the respondents answer whether they ‘(personally or jointly) currently hold an unsecured bank loan/a credit card’. Two dummy variables were created: one related to unsecured personal loans and the other to credit card debt. It is noteworthy that holding a credit card does not necessarily imply credit card usage. Similarly, compared to other developed economies, the number of credit cards in Spain has sharply increased in recent years due to, among other factors, the increase in the number of points of sale (POS) terminals in commercial establishments, the possibility of paying without a minimum cost, the growth of online purchase, and the development of contactless payment systems. Thus, in 2017, the Spanish population held 52.35 million credit cards; a non-negligible figure compared to 43.49 million in 2007, the year before the global financial crisis started (Banco de España, 2022). However, from 2017 onwards, credit card holding declined to 38.55 million in 2021, while debit card holding rebounded to 49.44 million. Credit cards were mainly used for deferred payments (Banco de España, 2019) rather than as ‘pure credit cards’. In sum, the previous facts lead us to consider that credit card holders are likely those who least need to borrow. Seven additional dependent variables were created to distinguish between individuals ‘entitled to hold debts’ and ‘actual borrowers’. These variables were taken from the ‘Expenditure and financial fragility’ module of the Survey of Financial Competences’ questionnaire. Namely, the dependent variables were created based on some of the possible answers to the following question: ‘What did you do to make ends meet the last time this [i.e., income did not cover your Table 2 Definition of independent variables Variable Definition Gender Dummy variable on the respondent’s gender: female (1); male (0) Age Continuous variable: logarithm of the respondent’s age in years Employment status Categorical variable on the respondent’s employment status: (1) employed or self-employed [reference category]; (2) unemployed; (3) retired; (4) another situation Income level Categorical variable on the total annual gross income of the household: (1) < €14,500 [reference category]; (2) €14,500–45,000; and (3) > €45,000 Educational attainment Categorical variable on the highest level of formal education of the respondent: (1) no formal education [reference category]; (2) completed primary education; (3) first stage of secondary education; (4) second stage of secondary education; (5) technical/vocational education; (6) university education Dependent children Dummy variable: the respondent normally lives in his/her household with either his/her own children under the age of 18 or those of his/her partner/spouse (1); otherwise (0) Marital status: married Dummy variable on the respondent’s marital status: the respondent usually lives in his/her household with his/her partner/spouse (1); otherwise (0) Financial literacy Categorical variable, ranging from 0 to 3 [0 being the reference category], on the number of correct answers to the following three questions on financial literacy: (a) Let’s suppose you deposit €100 in a savings account with fixed interest of 2% per annum. In this account there are no commissions or taxes. If you make no deposit or withdrawal, once the interest has been paid to you how much money will there be in the account after five years? [Over €110/Exactly €110/Less than €110/it is impossible to say with the information given/Other answers] (b) It is usually possible to reduce the risk of investing in the stock market by buying a wide range of stocks and shares [True/False] (c) Imagine that five siblings had to wait a year to obtain their share of €1,000, and that inflation that year was 1%. Within one year they will be capable of buying… [More than they could today with their share of money/The same amount/Less than what they could buy today/…] Self-control Continuous variable on the respondent’s self-control constructed by applying the EFA (see Sect."Measures of financial self-control" for more information) Two additional variables on self-control (i.e. the dummy variable SELFC_D and the categorial variable Q#.SELFC_D) were defined Current Psychology 1 3 living expenses] happened?’. The interviewees can choose the option of getting into debt, allowing the questionnaire to select among different types of indebtedness (e.g., borrowing from family and friends, pawn belongings, drawing down an already existing credit facility…). Table4 defines these seven dependent variables and the two additional ones focused on credit card holding and credit card use. Figure1 depicts the percentage of survey respondents with each type of debt, differentiating two categories: (a) debts held by households; and (b) debts held by individuals. Concerning the former, 56% of households have at least one credit card, and 17.6% have an unsecured bank loan. Concerning the latter, only 0.2% of individuals have obtained a loan from an informal provider (or Table 3 Socio-economic and demographic characteristics of survey respondents In the case of dummy and factor variables, the value of the mean reports the percentage of people who fulfil the condition according to which those variables take the value equal to 1. Age variable is not expressed as a logarithm. Obs. and Std. Dev. stand for the number of observations and the standard deviation, respectively Variable Obs. Mean Std. Dev. Gender: female 8,554 0.502 0.500 Age 8,554 47.225 15.774 Employment status Employed or self-employed 8,553 0.531 0.499 Unemployed 8,553 0.138 0.345 Retired 8,553 0.161 0.367 Another situation 8,553 0.169 0.375 Income level < 14,500€ 7,720 0.361 0.480 14,500–45,000€ 7,720 0.505 0.500 > 45,000€ 7,720 0.134 0.341 Educational attainment No formal education 8,552 0.024 0.152 Completed primary education 8,552 0.150 0.357 First stage of secondary education 8,552 0.272 0.445 Second stage of secondary education 8,552 0.228 0.419 Technical/vocational education 8,552 0.104 0.305 University education 8,552 0.223 0.417 Dependent children 8,551 0.307 0.461 Marital status: married 8,551 0.657 0.475 Financial literacy All answers are wrong 8,303 0.133 0.339 One correct answer 8,303 0.335 0.472 Two correct answers 8,303 0.343 0.475 Three correct answers 8,303 0.189 0.391 Table 4 Definition of dependent variables Variable Definition Unsecured personal loan Dummy variable: the respondent has, personally or jointly, an unsecured personal loan (1); otherwise (0) Credit card holding Dummy variable: the respondent has, personally or jointly, any credit card such as Visa, Mastercard, American Express… (1); otherwise (0) Family loan Dummy variable: the respondent has borrowed from family members or friends (1); otherwise (0) Employer loan Dummy variable: the respondent has asked for an advance or has obtained a loan from his/her employer (1); otherwise (0) Pawn belongings Dummy variable: the respondent has pawned his/her belongings (1); otherwise (0) Existing credit facility or overdraft Dummy variable: the respondent has drawn down an existing credit facility, or has used an authorised overdraft (1); otherwise (0) Credit card use Dummy variable: the respondent has used a credit card for cash advances, to pay bills, or to buy food (1); otherwise (0) Loan from financial services provider Dummy variable: the respondent has taken out a personal loan from a financial services provider, including banks, credit unions… (1); otherwise (0) Loan from informal provider Dummy variable: the respondent has taken out a loan from an informal provider/lender (1); otherwise (0) Current Psychology 1 3 moneylender), and 0.7% have pawned some belongings. Regarding credit granted by financial providers, 1.5% have applied for a new credit line, 1.3% have drawn down an existing credit line or used an authorised overdraft to make ends meet, and 2.3% have borrowed from an existing credit card. Finally, concerning other loans, 1.3% of the sample have borrowed from their employers or have asked for an advance, and 8.8% have asked for money from close relatives (family members or friends). Measures offinancial self‑control As previously mentioned, the main independent variable deals with the individual’s level of self-control. Previous studies have employed measures of self-control based on general behaviours rather than on financial matters (e.g., Achtziger etal., 2015; Wang etal., 2011). To construct a measure of self-control in financial matters, an EFA was carried out. In this regard, the Survey of Financial Competences assesses financial attitudes by asking individuals to identify the extent to which their behaviour corresponds to that described in several short statements. According to Ameriks etal. (2007) and Gathergood (2012), the selected statements should specifically refer to individual behaviours concerning financial choices rather than general behaviours to obtain a financial-oriented measure. The statements that compose the latent dimensionality of self-control are the following ones: (1) ‘I tend to live for today, without thinking about the future.’ (2) ‘I prefer spending money now to saving it for the future.’ (3) ‘Money is there to be spent.’ The answer options range from 1 (completely agree) to 5 (completely disagree). Following Gathergood (2012), the first statement could be labelled as a ‘heavy discounter’ statement, whereas the other two statements reflect impulsive spending behaviour (‘impulsiveness’ statements). In our sample, 18.3% of respondents agree or fully agree with living for today without overthinking about the future; 14.4% agree or completely agree that it is more satisfying to spend money than to save it for the long term; and 38.7% report that money is there to be spent. Gathergood (2012) reported that 9.2% of UK respondents agree or totally agree with the ‘impulsiveness’ statement. However, in this study, the percentage of Spanish individuals that agree with any of the two ‘impulsiveness’ statements is much higher. Exploratory Factor Analysis (EFA) and, particularly, principal component assessment is used as an extraction methodology to simplify the factor structure. Varimax is used as a rotation method. The commonalities were above 0.53, which suggests that the items effectively describe the variance of the original items. The factor explains 58.43% of the scale total variance, and the items have loadings ranging from 0.73 to 0.80. To contrast the internal consistency of the scale, following the recommendations of Trizano-Hermosilla and Alvarado (2016), the McDonald’s omega (0.65) Fig. 1 Percentage of survey respondents by type of debt 17.6% 56.0% 8.8% 1.3% 0.7% 1.3% 2.3% 1.5% 0.2% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% Unsecured personal loans Credit card holding Family loan Employer loan Pawn belongings Existing credit facility or overdraft Credit card use Loan from financial services provider Loan from informal provider Debts held by the household Debts held by the individual Current Psychology 1 3 and Cronbach’s alpha (0.64) coefficients are considered, both of which reflect acceptable reliability (Dunn etal., 2014; Nájera Catalán, 2019). Table5 summarises the primary EFA information. Confirmatory Factor Analysis (CFA), in which items with high factor loadings more accurately represent the latent variable in the model (in this case, self-control) and those that are weakly correlated with others poorly define the variable (Nunkoo etal., 2013), was conducted to validate the factor. Hence, as scale reliability was confirmed, a new original variable (SELFC) that combines the three items was created. Specifically, from the combination of the three items, considering the corresponding weights and weightings, a factor score was obtained for each individual. Low or negative values for the new variable denote low levels of self-control in financial matters, while high or positive values denote high levels of self-control. Then, two additional variables were created using the self-control score of individuals as a starting point. First, the variable SELFC_D is a dummy variable taking the value 1 for those individuals whose estimated self-control score (SELFC) is above the median level of self-control and 0 otherwise. Second, the variable Q#.SELFC is a categorical variable on the quartiles determined by the individual’s selfcontrol score, with 1 being the lowest level and 4 the highest. Characteristics ofindividuals according totheir level ofself‑control infinancial matters Focusing on self-control in financial matters (see Table6), 52.4% of survey respondents possessed a level of self-control above the median in the sample. Among them, most are women (53.6%) with an average age of 47years, married (69.6%) and without children under 18years living at home (65.5%). Concerning individuals’ education, 48% of the sample has completed secondary education – either the first or the second stage – and 25.4% has completed university studies. By contrast, individuals with low levels of self-control (i.e., those below the sample’s median level of self-control) seem to display lower educational attainments. In turn, both subsamples are similar regarding their financial literacy, income level, and employment status. Regarding borrowing behaviour, although more than half of the respondents have credit cards, few individuals with a self-control score below the median use their credit cards (3.2%), and even fewer individuals with self-control scores above the median use them (1.6%). Individuals with lower self-control levels seem to prefer using informal credit and asking for money from their family members or friends (10.3%). The differences are more pronounced when respondents are grouped into the quartiles determined by the individual’s selfcontrol score. Most of the individuals with the lowest level of self-control (Q1) are men (56.5%), whereas most of the individuals with the highest level (Q4) are women (56.6%). In both cases, most individuals are employed or self-employed, even though the employment rate is higher among individuals with the highest level of self-control (56.6% versus 48.1%). Similarly, those individuals with the highest level of self-control (Q4) seem to have attained higher levels of formal education. In this regard, 24% of them have completed university studies (16.6% in the case of individuals with the lowest level of selfcontrol). In contrast, there are no considerable differences in terms of financial literacy. Concerning indebtedness behaviour, a greater percentage of individuals with the highest level of self-control (Q4) have credit cards (58.9% versus 52.5%). However, when it comes to their use, those with the lowest level of self-control (Q1) use them more (3.9% versus 1.4%). The results evidence that the difference between credit card ownership and credit card use is particularly noticeable. Furthermore, individuals in the lowest quartile of self-control have the highest propensity to borrow from family and friends (12% versus 7.4%). Econometric model andestimation In order to analyse whether self-control constitutes a driving force of household borrowing behaviours, nine econometric models were run. Individuals’ debts were modelled as latent variables. Each type of debt was related to self-control measures through the following specification: where i denotes the index for the sample individuals, Y∗ i refers to each type of debt as a latent variable, SELF represents the scale of individuals’ self-control (SELFC) and, alternatively, the dummy variable (SELFC_D) and the selfcontrol quantile variable (Q#.SELFC), and Xi includes the Y∗ i =𝜙(𝛽0+𝛽1SELF i +𝛽 j X i) Table 5 Factor analysis and reliability of self-control scale Statement Cross factor loadings McDonald’s omega Cronbach’s alpha ‘I tend to live for today, without thinking about the future’ 0.76 0.65 0.64 ‘I prefer spending money now to saving it for the future’ 0.80 ‘Money is there to be spent’ 0.73 Current Psychology 1 3 remarks, consumers who suffer from self-control problems are more likely to get over-indebted, as they make more use of high-cost credit (namely, high-cost credit accessible at short notice and/or at the point of sale) and to be more exposed to financial distress. Questionnaires to customers and even interviews with relatives could be another way to detect this time inconsistency problem early. In fact, individuals’ outstanding debt can also put their relatives at risk of insolvency. Besides, consumers require adequate financial empowerment to make appropriate financial decisions (Nam, 2022). To this end, as Mawad etal. (2022) suggest, policies should help young adults acquire skills in self-control as well as integrating financial knowledge into school curricula. In this regard, as Boto-García etal. (2022) also advocate, financial socialization, understood as the exposure to financial concepts while growing up, becomes essential. Limitations andfuture research directions Despite its contributions, this study presents some limitations. Firstly, the dataset has a cross-sectional nature, obstructing longitudinal analysis and the possibility of drawing causal relationships. This limitation may be overcome when new editions of the survey are published. Secondly, the use of secondary data has limitations, such as time lags (up to six years in our case). However, no other survey would allow us to analyse this topic with more up-to-date data. Thirdly, future studies should broaden the portfolio of possibilities concerning household debt. In this regard, it is vital to bear in mind that some of the credit options here are ‘necessary’ (not voluntarily chosen) loans to make ends meet and cover living expenses. Therefore, this type of debt is not the most appropriate to explore the potential effect of self-control. In such cases, self-control might fail as a driving force, as identified for some credit options like pawning belongings or asking for a loan from an informal provider. And fourthly, the use of self-reported measures of self-control could constitute a pitfall, due to their higher susceptibly of psychological distortions, as already suggested by Jia etal. (2022). In this regard, future studies should complement self-reported measures with behavioural measures (Dang etal., 2020). Combining these measures to assess self-control has already been done in contexts other than the financial one (e.g., Boon-Falleur etal., 2022). This paper opens the way for further research. In this regard, future studies might benefit from considering continuous variables on debt. Even though the Survey of Financial Competences is quite comprehensive, it does not provide any information on the amount of debt its respondents hold. This study can also be extended by addressing other issues that contribute to self-control (e.g., locus of control, time preferences…). The consideration of additional variables and new econometric models (e.g., structural equation modelling) would also allow us to assess whether other theoretical approaches (e.g. the theory of planned behaviour, as suggested by Nunkoo and Ramkissoon (2010)), could contribute to explaining the importance of self-control when making borrowing decisions. In sum, as Mansfield etal. (2003) acknowledge, self-control is just one of the pieces of the complex puzzle of consumer borrowing patterns, so more research on this topic is still needed. Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Data availability statement The data that support the findings of this study comes from the Survey of Financial Competences, that is a joint initiative of the Banco de España and the Comisión Nacional del Mercado de Valores. Restrictions apply to the availability of these data, which were used under license for this study. Data are available at https:// app. bde. es/ pmk/ en/ ecf/ 2016 with the permission of the Banco de España. Declarations Disclosure statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of interest The authors declare that they have no conflict of interest. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. 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