A Composite Index to Measure the Financial Well-Being of Low-Income Workers in Real Time for Improved Personal Financial Management

By: Ibrahima II Diallo
DBA Program
Department of Finance, School of Management
Université de Sherbrooke

Copyright © 2021 Ibrahima II Diallo. All rights reserved.

Introduction

Since the initial work of Campbell, Converse and Rodgers (1976) on the quality of American life, the financial well-being (FWB) of populations has been a topic of political and academic interest. The reason is that high financial distress and low FWB have combined impacts on health and job productivity (Prawitz et al., 2006). This is why agencies such as the Financial Consumer Agency of Canada (FCAC) and the Consumer Financial Protection Bureau (CFPB) conduct FWB surveys to facilitate the implementation of social safety net and financial literacy policies (see the FCAC’s Financial well-being in Canada: Survey results for more details). For example, the results of the FCAC’s Financial Well Being in Canada survey in 2019 indicate an average score of 66 out of 100. The results of these surveys provide a general idea of the population’s FWB on a specific date, but mask disparities between regions, social groups and even individuals. Therefore, to improve personal money management without a financial planner, each person would need to have immediate access to their FWB score. This may be possible with the emergence of financial technology (fintech), but only if FWB is well-defined and measured.

To be consistent with that dynamic, this paper proposes a composite index that can measure the FWB of individuals in real time, specifically for low-income Canadian workers between the ages of 30 and 50. Members of this group are of critical interest, as they face money management challenges in the early stages of their careers (Vlaev and Elliott, 2014). Additionally, their FWB may be influenced by the FWB of emerging adults (Sorgente and Lanz, 2017). Lastly, although the average FWB score is 66, a significant number of Canadians are regularly stressed about money (FCAC [ACFC], 2019). Stress affects working Canadians of all income levels and age groups. For example, 48% reported losing sleep over financial worries (FP Canada, 2018) and 45% said they were experiencing stress related to personal or household finances (Sun Life Financial, 2017).

This research paper is divided into four sections. The first section discusses the issues of measuring FWB to ensure financial health. The second section provides a summary of various ways to define and measure FWB, based on a thorough analysis of existing academic and applied research. This justifies a need to explore other ideas for improving the concept, which is the purpose of the third section, which presents a method for understanding the FWB of low-income workers. Lastly, the fourth section highlights the strategy for gathering data to test this method and demonstrate its effectiveness through a mixed method (quantitative and qualitative) research approach.

Measuring financial well-being: A matter of financial health

General well-being is of particular interest to policymakers as well as academics, who have been trying for decades to identify and define the multidimensional concept (Brown et al., 2014; Dalziel, 2019; Moll et al., 2015; Montpetit et al., 2015; Weinstein and Stone, 2018). The life cycle theory of Ando and Modigliani (1963) laid the foundation for discussion on the financial aspect of well-being, which gained momentum with the work of Campbell et al. (1976). Since then, it has been central to the analysis of well-being. For example, Statman (2019) states: “Financial well-being underlies well-being in all its domains [finances; family, friends and communities; work and activities; and health, both physical and mental], and well being in each domain is impossible without financial well-being” (p. 49). Recognizing this, the governments of some developed countries have implemented extensive social protection programs (such as pension plans, minimum wage and tax free banking products) and set up financial protection agencies to ensure a better quality of life for their people. However, in recent years, individuals themselves are increasingly taking responsibility for their own social protection in the wake of increased competition in the financial services industry (Kempson et al., 2017). Government agency survey results are used to build comprehensive social welfare policies, but are not well-suited for use in rigorous personal financial management by individuals. FWB is primarily a personal matter. Therefore, as in medicine with the glucose meter or electronic blood pressure monitor, the world of finance should provide each person with a tool that can instantly provide their FWB score. This tool would help people make better decisions in managing their personal finances, promote better budget management, and generate significant income by reducing the need for a financial planner.

Low-income workers may have some management skills, but do they have adequate tools to effectively carry out this demanding and high-risk responsibility? No. However, emerging fintech can help to achieve this goal. Fintech is revolutionizing the financial services industry (Panos and Wilson, 2020), spurring the creation of new tools for consumers, such as mobile payments, and personal investment and wealth management platforms (Frost et al., 2019). According to Carlin et al. (2019), these innovations have reduced high-interest unsecured consumer debt and bank fees by 14% through access to information about transactions and bank account balances.

However, operationalizing the concept of FWB in these technology-based tools presents a major problem, namely, how to accurately define FWB and measure it in real time. Currently, there is no consensus on the definition and dimensions of FWB (Kempson et al., 2017; Salignac et al., 2020). In addition, users of fintech tools would find it tedious to have to answer multiple sets of questions in national surveys to find out their score. For example, the FCAC survey, based on the model of Kempson et al. (2017), determines FWB on the basis of five categories of factors (FCAC [ACFC], 2019)^{Footnote 1}  that are measured using some 40 related variables. An alternative that is currently available is the CFPB (2015) model, which has a standard 10 question version and a shortened 5-question version. Unfortunately, this model is subjective and therefore depends solely on the respondents’ answers. In addition, it fails to take into account the objective and psychological dimensions of FWB. Yet, as some have pointed out (Joo and Grable, 2004; Tenney and Kalenkoski, 2019), these two additional dimensions, accounted for in the Kempson et al. (2017) model^{Footnote 2} , make it possible to better define and measure FWB. Moreover, Kempson et al. note the need “to revise [their] conceptual model” (p. 48) through structural equation modelling, to explore the interrelationships indicated in their original model and to reflect the empirical findings.

Therefore, in an increasingly complex Canadian financial landscape where low-income working Canadians (the target group) deal with multiple resource systems, we can seek to develop, with as few variables as possible, a simple but robust method of measuring FWB in real time that incorporates all three dimensions and that can be used to guide the management of a personal budget (the research priority). With this in mind, three key questions (research themes) will be explored in detail. First, which definition of FWB best fits our situation? Second, what are its determining factors? Lastly, once these factors have been identified, which ones can be used to create an index suitable for fintech platforms to enable users to determine, in real time, their short-term (current), medium-term and long-term (future) FWB?

But how is the concept of FWB analyzed in the academic and applied literature? The second section of this research paper seeks to answer that question.

Financial well-being in academic and applied literature

Campbell et al. (1976) paved the way for many FWB studies that cover a number of areas, such as financial advice and planning (Bergeron et al., 2003), budget management, consumer decision-making and service marketing, and responsible finance in the fintech era (Panos and Wilson, 2020). Most of these studies draw on existing theories, such as the life cycle theory (Ando and Modigliani, 1963) and the life course theory perspective (Elder, Johnson, and Crosnoe, 2003), in an attempt to define FWB and understand its various dimensions. In these studies, the researchers most often use positive paradigms similar to FWB (financial health, financial satisfaction, or economic well-being) or negative paradigms such as financial distress or financial stress (Sorgente and Lanz, 2019). Thus, FWB is a complex and multidimensional concept (Sorgente and Lanz, 2017) that today must be studied in three dimensions (Kempson et al., 2017): subjective, objective and psychological. These dimensions also include a set of measurements (subjective, objective and psychological) for assessing FWB.

The subjective dimension or subjective FWB (Sun Life Financial, 2016) consists of “an individual’s subjective experience with respect to his/her financial condition and the manner in which he/she evaluates such condition” (Sorgente and Lanz), 2017, p. 283). Subjective measurement of FWB thus amounts to asking direct questions (Bravo et al., 1996) where respondents provide feedback on their positive and negative feelings of well-being as predicted by financial satisfaction and respect (Ng and Diener, 2014). There is an apparent lack of objectivity in the emotional aspect of this measurement, since it is based on self-assessment. For this reason, the objective dimension of FWB is added to the analysis, to find an alignment between the objective measurements found on financial statements and subjective measurements of clients’ perceptions of FWB (Tenney and Kalenkoski, 2019). The objective dimension of FWB (or objective FWB) concerns how a person manages their personal finances. In practice, it corresponds to financial health (Sun Life Financial, 2016), while in theory it is called economic well being (Sorgente and Lanz, 2017), which covers three different aspects: (1) the individual’s resources or inputs (income, financial assistance); (2) expenditures or outputs (debt, purchases of goods and services); and (3) whatever the individual already owns (assets, savings account, health insurance, job benefits, financial education). For example, in PwC’s (2019) survey, 77% of respondents reported having a good understanding of employer benefit and savings plans and the role those plans play in their overall FWB. However, Joo and Grable (2004) suggest that the psychological dimension be considered when studying FWB. The 2019 FCAC survey demonstrates the significance of psychological factors, such as impulsivity and self-control, among the variables that influence the key drivers of FWB. The results of this survey showed that, among Canadians, 12% of their FWB score was related to psychological factors, and these factors alone accounted for 21% of the variability in active saving scores. Moreover, the findings of Kempson et al. (2017) recommend exploring the relationships between FWB and underlying psychological factors such as personality traits and attitudes. Indeed, the literature shows that personality traits affect the behaviour of investors (Akhtar et al., 2018; Zhao et al., 2010) and the performance of money managers (Donnelly et al., 2012), which impacts their FWB. For example, Donnelly et al. (2012) showed that conscientiousness and neuroticism are the two Big Five personality traits that are strongly linked to money management, with conscientiousness being positively linked and neuroticism, negatively linked.

To date, there are abundant definitions and measures^{Footnote 3}  of FWB that, unfortunately, are not in agreement (Kempson et al., 2017; Salignac et al., 2020; Sorgente and Lanz, 2019). Some focus solely on the objective domain (Joo and Grable, 2004) or the subjective domain (Brüggen et al., 2017), while others place more emphasis on a combination of both (Porter and Garman, 1992) or incorporate psychological aspects as a third component of FWB (Kempson et al., 2017). Disagreement also stems from the use, rightly or wrongly, of synonyms such as economic well-being, financial health, financial satisfaction and income satisfaction to measure FWB (Sorgente and Lanz, 2017). This disagreement creates opportunities for research into this crucial concept. It especially poses a problem for the fintech industry, which is increasingly looking to create FWB using artificial intelligence. For example, Barasch (2018)^{Footnote 4}  notes that there are shortcomings in financial institutions’ FWB and personal financial management offerings for consumers. Most of the products on the market work for expert users, but not for non experts. Furthermore, banks have not yet provided their clients with tools to make informed decisions (Vlaev and Elliott, 2014).

Consequently, enabling every person to instantly find out their FWB score is a worthy goal, to improve personal money management without the need for a financial planner. This is a real consumer need, given that the survey results from Envestnet | Yodlee summarized by Barasch (2018) show that 79% of 22- to 34-year-olds and 77% of 35- to 49-year-olds were moderately to extremely interested in using a virtual financial wellness coach (VFWC), while 62% of people 50 and older were moderately to extremely interested in doing so. In addition, the authors found that consumers ideally want simple tools that measure their financial health in real time and perform dynamic calculations to display scores that reflect behaviour changes in real time. That is the objective of this paper on developing a real-time FWB measurement index, specifically for low income working Canadians who use fintech platforms to manage their personal finances. With this goal in mind, the financial well being triad model is proposed as a way of understanding FWB.

Improving financial well-being: Financial well-being triad

Although there are a number of definitions of FWB (Kempson et al., 2017; Salignac et al., 2020), those that include all three dimensions (objective, subjective and psychological) at the same time are rare in the literature. Moreover, Holzmann et al. (2013) identifies two relevant domains of financial capability: controlled budgeting and making provisions for the future. However, the authors state that these two domains should not be collapsed into a single score, as is the case with several indices, for a comparable level of financial capability across different settings. Similarly, the CFPB (2015) notes four important elements of FWB: (1) financial security; (2) freedom of choice; (3) the present; and (3) the future. On the basis of all these observations and initial readings, a definition incorporating the three dimensions of FWB is proposed from the theoretical life course perspective of Elder et al. (2003). FWB is a state in which an individual (i) trusts their own judgment in recognizing their own strengths and weaknesses in making financial decisions for their present and future fulfillment; (ii) uses their resources to cover their current (short-term) commitments and needs; and (iii) has enough financial resilience to absorb medium- and long-term financial shocks and to have freedom in their finances. This definition divides FWB into three components (the FWB triad) that are useful for budget management (see Appendix, Figure 1): (1) short-term FWB (present); (2) medium-term FWB; and (3) long-term FWB (future). Short-term FWB affects the other two components, while medium-term FWB affects long-term FWB. However, some people may, over their lifetime, either favour one over the other two or seek an optimum combination of all three. The triad approach as a way to improve FWB is based on a comprehensive conceptual model (see Appendix, Figure 2) derived from a preliminary review of several models (Gerrans et al., 2014; Joo and Grable, 2004; Kempson et al., 2017; Porter et al., 1992; Sorgente et al., 2019; Tenney et al., 2019; Vlaev and Elliott, 2014). In this model, the individual’s psychological behaviour drives the dynamics of FWB. It incorporates the strengths and weaknesses captured by the personality traits of the Big Five model (Costa Jr. and McCrae, 2008). The individual’s psychological behaviour (personality trait) affects financial coverage (the individual’s ability to meet current commitments and needs with current resources), financial resilience (the existence of financial resources in the medium term) and protection for the future (the existence of long-term funds) to deal immediately with a foreseeable or unforeseeable financial shock, as well as FWB itself. Financial coverage and financial resilience affect short-term FWB and medium-term FWB, respectively, while protection for the future (long-term financial coverage) affects long-term (future) FWB. For example, with respect to income, Brown et al. (2014) notes that the most important variable in financial satisfaction is employment income for younger individuals, but investment income for older individuals. Although an agent’s rationale may be imperfect or constrained by limited cognitive abilities, Berton (2016) acknowledges that instrumental or substantive rationality leads the economic agent to adapt their resources and managerial behaviours as best they can to the constraints they face. Accordingly, it is assumed that objective measures (financial ratios of coverage, resilience, and protection for the future) can capture these behavioural changes, making these factors important variables in the study of FWB (Garrett and James, 2013; Tenney and Kalenkoski, 2019). Indeed, financial ratios positively affect financial satisfaction (Garrett and James, 2013) or perceived FWB (Tenney et al., 2019). However, the FCAC (2019) report states that FWB may be influenced by certain sociodemographic factors (age, household size, number of children), economic factors (income, employment status), and macroeconomic policies (tax policy, monetary policy), since these have an effect on financial ratios and are controlled variables in Donnelly et al. (2012). For example, the number of children may play a role in the various stages of life, as savings tend to decrease with the number of children in the family home and increase as the number of children decreases (Ando and Kennickell, 1985; Blinder et al., 1983).

The FWB index will be developed according to the 10 steps of Mackenzie et al. (2011) and will be implemented in collaboration with a Canadian start-up whose role will be to (1) create the algorithms to incorporate the relevant index variables identified and measure, in real time, the effect of those variables and behaviours; (2) possibly upgrade the algorithms based on the preliminary test results; and (3) customize personal finance management for each user. The budget management tool resulting from the operationalization of the triad will foster a better life for the user, who will maintain good financial health, achieve financial security and acquire the appropriate skills to make sound decisions. Its ability to detect risky behaviours will enable the user to predict and adjust their actions and behaviours to mitigate the impact on personal finances, health and day-to-day stress. Its approach will have a great impact on the financial health of users, especially Quebecers and Canadians, by making them aware of their relationship with money, their financial health and the impact of their actions on their FWB in the short, medium and long term.

The nature of the test requires the use of a mixed method (qualitative and quantitative) approach. Its methodology will highlight the ethical implications of this research, the rationale for using predictive and confirmatory correlational designs, the identification of the study population and sample, the description of concept measures, and the strategy for data collection and analysis. The magnitude of this strategy calls for a more detailed description of its constituent elements. That is the purpose of the following section.

Data gathering and processing strategy

The data gathering and processing strategy is divided into several distinct phases as follows:

Conduct a scoping review to conceptualize the FWB triad – The scoping review, including systematic steps (Arksey and O’Malley, 2005; Colquhoun et al., 2014; Levac et al., 2010), will be used to identify, gather and synthesize existing knowledge about FWB. The methodology proposed by these authors will inform the process of extracting peer-reviewed articles on the basis of appropriate keywords applied to various databases, grey literature and academic journals. The inclusion criteria will be as follows: (1) relevant studies must deal with financial well-being; (2) articles must be written in English or French; (3) articles must have been published between 2001 and 2021; and (4) articles must specify research methods (qualitative or quantitative). This step will make it possible to validate the preliminary definition proposed above or to propose a more accurate definition.

Consult with an expert panel to adjust and confirm the definition of FWB and conduct the pilot test – This step will add methodological rigour to the scoping review (Levac et al., 2010), and the four phases suggested by Colquhoun et al. (2014) will be followed to ensure maximum effectiveness. First, a clear objective for the consultation will be defined, including an examination of the scope and relevance of the concept definition and its validation. The preliminary results will then be used to inform the consultation. In addition, to ensure clear articulation, the panel of experts will be recruited using the snowball technique and the following criteria: expertise in responsible finance or positive psychology and active participation in research in at least one of these two areas. There will be two phases of consultation, the findings of which will be combined to produce the overall index. First, each subgroup of four experts will be consulted as a focus group, remotely, using specialized IT tools. Second, we will conduct an online survey asking experts to rate each concept from the scoping review on a five point Likert scale, from “strongly disagree” to “strongly agree.” The last phase involves setting up a mechanism for knowledge transfer and exchange with stakeholders in the field. It will be accompanied by a pilot test with a small group selected at random from a sample of the general population. This activity is designed to test the index and adjust it as needed for operationalization.

Conduct the survey in the general population, analyze the data and perform statistical tests to create the index – A survey will be conducted with a large sample drawn from the population and the data obtained will be processed and analyzed quantitatively using statistical software (e.g., SPSS or EViews). First, descriptive statistical analysis will be used to extract quantitative information useful for data validation. Principal component analysis (PCA) will then be used to validate, on the basis of the database, the constructs of the conceptual model. In addition, the structural equation model (SEM) is used to predict the perceived pursuit of FWB. The SEM results will be used to test the set of assumptions made (Gerrans et al., 2014), that is, the interrelationships identified in the conceptual model. These methods are part of the statistical analyses often used in FWB studies (Sorgente and Lanz, 2019). Lastly, the logistic regression model will be used to validate the robustness of the SEM, which primarily helps to avoid potential biases associated with analyzing Likert-type scale variables (Tenney and Kalenkoski, 2019). Figure 3 in the Appendix shows the operational framework without the demographic, social and economic factors, which will, however, be incorporated into an econometric model along with all the other variables, in order to select the most relevant ones for constructing a simple, robust composite index for measuring FWB in real time. With the FWB triad approach, three sub-indices will be calculated: short-term, medium-term, and long-term FWB indices. The final FWB index is the mean (arithmetic or harmonic) of the three sub-indices and is broken down into five categories: low FWB, moderate FWB, average FWB, high FWB and very high FWB. The indices may be weighted, if necessary, using the Canadian Index of Wellbeing (CIW) weighting principle, which assigns equal weight to each sub-index, since there are many reasons why one indicator may be favoured as more important than the others; however, it is more difficult to find the right reason for such an approach (CIW, 2016) especially since, over the course of their lives, individualshave different interests in different components of the FWB. However, users will be able to assign a weight of their choice, based on their own perceived importance of the type of FWB.

Conduct validation interviews to finalize the index – The validation interviews will be conducted with some of the participants from the original survey sample who (1) have a level of FWB above or below the median of the original survey sample; (2) are willing to be interviewed remotely via Internet-based video communication software or telephone; and (3) speak English or French. The recruitment process will run concurrently with the interview analysis process. The 45- to 90-minute semi-structured individual interviews will be conducted to further explore and clarify the issues raised (Bolderston, 2012). Before the start of each recorded interview, the participants’ oral informed consent will be obtained. The interview guide, prepared in accordance with and focusing on the main theme of their experience of FWB throughout their lives, will consist of a series of open-ended questions designed to elicit statements about the experience of FWB or lack thereof (with specific examples to support the statements). It will remain flexible to allow for probing questions to explore tangential areas that may arise. Empirical saturation regarding the topic of primary interest will be reached when no new data (or information) relevant to the validation of the FWB index is observed. NVivo 11 will be used to organize, store and analyze data from the recordings (field notes on the conduct of the interview, non-verbal and attitudinal behaviours of participants, and salient content items). Digital audio transcriptions may be done by a qualified transcription professional. Thematic content analysis will then be used to code and compare the data in relation to the study objectives, that is, the validation of the index. In this study, the themes generated are deductive, as they will be guided by the statistical model developed, which is the index created.

Conclusion

This paper seeks to implement a real-time FWB measurement index that a start-up may integrate into its personal financial management fintech platform. By incorporating the three dimensions of FWB (objective, subjective and psychological), the tool will enable a low-income worker to find out their FWB score in real time. This will have both managerial and academic implications. From a managerial point of view, the index solves a critical problem, that of operationalizing virtual financial wellness coaches. It will help financial planners upgrade their FWB analysis and assessment tools. By answering various research questions, this paper fosters a significant shift in the knowledge frontier through an alignment between the three FWB measurements and the development of a composite real-time FWB measurement index.

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Appendix

Figure 1: Financial well-being triad

Figure 2: Financial well-being triad conceptual mode

Source: Adapted from several studies (Joo and Grable, 2004; Kempson et al., 2017; Porter et al., 1992; Sorgente et al., 2019; Tenney and Kalenkoski, 2019; Vlaev and Elliott, 2014)

Figure 3: Operational framework of the financial well-being triad conceptual mode