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date: 03 July 2022

Household Financefree

Household Financefree

  • Sumit Agarwal, Sumit AgarwalFinance, National University of Singapore
  • Jian ZhangJian ZhangFinance, University of Hong Kong
  •  and Xin ZouXin ZouFinance, Hong Kong Baptist University

Summary

Households are one of the key participants in the economy. Households provide land, labor, and capital to the external economy, in exchange for incomes including rents, wages, interests, and profits; the incomes are then utilized to buy goods and services from the external economy again, rendering an income flow circular. This suggests that households make complicated decisions in almost all areas of economics and finance, which constitute the scope of household finance studies. Specifically, household finance encompasses the following three topics: (a) how households make financial decisions regarding saving, consumption, investment, housing, and borrowing; (b) how organizations provide goods and services to satisfy these financial functions; and (c) how external interventions (from firms, governments, or other parties) such as financial technology (FinTech) affect these financial activities.

Despite the important stake in the financial system, it was not until recent decades that household finance became a prosperous research field. For many years, financial studies mostly focused on financial markets, nonfinancial corporations, and financial institutions and intermediaries, with households being delivered as a simplified representative agent. Classical economic models do consider households in the economic system, but mainly focus on their functions in the income flow circular (i.e., the saving or demand for products). Recently, the household finance field received more attention and has produced a large strand of theoretical and empirical studies due to the incremental participation of households in financial markets, the observed consequences of events such as financial crises, the availability of more detailed high-quality granular data, and the regulations and interventions induced by technology innovation.

Subjects

  • Financial Economics

Saving

Worldwide History of Household Saving

Although many economies in the 21st century are consumption driven, most of them started with saving. A group of European philosophers in the 1700s–1800s began to advocate for frugality, and this ideological trend was also transmitted to the United States (Defoe, 1697; Franklin, 1963; Smiles, 1875). Banking systems were soon established to institutionalize the habit of saving. During the 1900s–1960s, European governments also used savings to finance national projects and war, and urged households to save for after-war reconstruction. From the 1960s on, European policies began to diverge, and Britain changed toward a consumption-driven economy as part of a cultural shift. Conversely, as little after-war reconstruction was needed, the United States became a more consumption-oriented economy in the 1950s, after a short golden age of saving in the 1930s–1950s. Saving further decreased from the 1980s until the 2008 financial crisis, which reminded households to save again.

Many Asian countries were colonies of European countries and followed the European saving model before independence, after which they evolved their own banking systems. Japan developed its own concept of thrift and kept on being savings oriented until the 1980s. Korea and Singapore developed their early-stage saving philosophy and banking system following Japan and evolved independently afterward. Because of wars and social transformation, Chinese households saved little from 1919 until the 1970s. With the massive demand for funding resulting from the economy opening up in 1978, Chinese leaders reopened the postal saving system, and Chinese household savings began to boom; China remained a high-saving society for many years to come.

Development of Household Saving Theories

The classical model assumes that with the exchange of income for goods and services, any income not spent is saved by households and then flows into the business sector as investments (Smith, 1776). Market equilibrium occurs when the economy is in full employment; savings are equal to the amount of investment under equilibrium, and hence they are positively related with the interest rate. However, the inability of the classical model to explain the failure of the market to self-adjust during the Great Depression in the 1930s led to the development of the Keynesian model (Keynes, 1936), which does not require full employment. In the Keynesian theory, saving and consumption are based on changes in (disposable) income instead of interest rates. If money invested is greater than money saved, a further increase in investment leads to higher income, which will raise both consumption and saving; whereas at low levels of disposable income, saving can be negative. Thus, the Keynesian model suggests that the MPS (marginal propensity to save, defined as the ratio of change in saving to change in disposable income) is a positive number between 0 and 1, and it should always be higher than the APS (average propensity to save, defined as the ratio of total saving to total disposable income). Hicks (1937) developed the IS–LM (investment saving–liquidity preference money supply) model to summarize Keynes’ general theory, which explicitly establishes the link between interest rate, saving, and income.

Empirical evidence shows that household saving and APS rise with income (in the short run), and MPS is less than 1, which is consistent with the Keynesian model. However, a conflicting fact was soon documented: In the long run, APS does not increase along with income over time (Clark, 1945). This called for alternative theories that extended household decisions to multiple periods. Friedman (1957) introduced the permanent income hypothesis (PIH), which suggests that saving in any period of life is determined not only by current income level but also by long-term expected income (i.e., permanent income) in the infinite future. In this sense, transitory income is predicted to have 0 marginal propensity to consume (MPC), and hence an MPS equal to 1 under the PIH. The life-cycle hypothesis complements the PIH but assumes that consumers plan consumption and saving over a finite time horizon—their own lifecycle (Modigliani & Brumberg, 1954).

Besides the intertemporal substitution and life-cycle motives for household saving considered by the PIH and life-cycle hypothesis, respectively, later models built in more saving motives. One such is the precautionary motive, where households save to cushion future unexpected events (Hubbard et al., 1995), and another is the bequest motive, where households wish to bequest their children (Browning & Lusardi, 1996; Kotlikoff, 1988). Some researchers suggest there is a hierarchy among different saving motives (Lindqvist, 1981; Sturm, 1983).

Factors Affecting Household Saving

Various factors are documented to affect household saving, including institutional, demographic, and socioeconomic characteristics, and government interventions can alter the saving rate by manipulating those factors.

Interest Rate

Interest rate affects household saving through two effects that move in opposite directions: income effect and substitution effect. The income effect suggests that an increase in interest rate leads to an increase in income, which reduces the need to save; whereas the substitution effect suggests that the increased interest rate makes the opportunity cost of consumption higher (as well as making a higher return for saving) and hence leads to more saving. The sign of the relationship between interest rate and household saving depends on the relative power of the two effects, and empirical studies document both. Weber (1975) and Friend and Hasbrouck (1983) found that an increase in the (real) interest rate led to less saving, or more consumption, suggesting that the income effect dominates. Conversely, studies also document that saving increases with (real) interest rate worldwide, which suggests that the substitution effect dominates (Agarwal, Charoenwong, et al., 2020; Boskin, 1978; Grigoli et al., 2018; Gylfason, 1981; Tullio & Contesso, 1986; Wright, 1969). There are also studies documenting no significant relationship between interest rate and saving (Agarwal, Chomsisengphet, Yildirim, et al., 2020; Agarwal, Pan, et al., 2020; Baum, 1988; Blinder, 1987; Hendershott & Peek, 1984; Howrey & Hymans, 1978).

Inflation can affect household savings by affecting the real interest rate and real value of assets. Specifically, inflation causes the real value of assets to fall, which induces households to save more through the income effect (Shinohara, 1982). On the other hand, the substitution effect may discourage household savings because more will be lost with higher savings. Moreover, inflation brings higher uncertainty to future income, which may affect consumer confidence, leading to higher savings and lower consumption (Grigoli et al., 2018; Howard, 1978).

Demographic and Socioeconomic Factors

Many demographic characteristics are found closely associated with household saving, but the relations are not necessarily causal. Some studies observed a negative relation between age and saving (Hayhoe et al., 2012). White households were found to have higher savings than black households (Yuh & Hanna, 2010). Married couples without children were found to have the highest saving, whereas single females were less likely to save than single males (Browning & Lusardi, 1996; Yuh & Hanna, 2010). Attanasio (1998) found that male-headed U.S. households save consistently more, whereas Denizer et al. (2002) found that in transition economies, households headed by females save more. More-educated individuals are found to be more likely to save (Yuh & Hanna, 2010). The effect of health on saving is also mixed: Yuh and Hanna (2010) documented that poor-health households are more likely to save, whereas Fisher and Montalto (2010) found that households with poor health save less. Factors such as financial literacy and education can potentially explain the heterogeneity across demographics (Bosworth & Bell, 2005; Hung et al., 2009; Lusardi & Mitchell, 2011).

Attanasio (1998) identified that different cohorts have different lifetime saving profiles, which can be explained by the productivity growth across generations (Jappelli, 1999; Kapteyn et al., 2005). Generally, higher income and wealth are associated with higher saving due to the income effect (Grigoli et al., 2018), but there is also evidence showing that improvement in wealth leads to lower savings (Shibuya, 1987). Countries that require a higher down payment for durable goods such as houses and cars (e.g., Japan) are found to have a higher saving rate (Grigoli et al., 2018; Hayashi, 1986). Financially literate individuals generally have a higher ability to make rational saving decisions (Agarwal, Driscoll, et al., 2009; Lusardi & Mitchell, 2014); and studies document that higher financial literacy among households leads to a higher likelihood to plan for retirement, which in turn leads to higher wealth accumulation (Agarwal, Amromin, et al., 2015; Lusardi & Mitchell, 2007a, 2007b).

Other factors such as cultural background (Strumpel, 1975), language (Chen, 2013; Roberts et al., 2015), and insurance accessibility (Summers & Carroll, 1987) may also lead to different saving behavior. Household savings also differ across countries (Edwards, 1996; Loayza et al., 2000).

Pensions

Some studies document that public pension schemes, social insurance, and Social Security reduce personal saving because of a substitution effect (Diamond & Hausman, 1984; Feldstein, 1974; Feldstein & Pellechio, 1979; Hubbard et al., 1995; Kotlikoff, 1979; Summers & Carroll, 1987). However, pension plans may incentivize more personal saving through a recognition effect, by letting the public know about the prospect of being financially independent after retirement (Barros, 1979; Feldstein, 1974). There are also studies documenting an insignificant impact of the public pension on private savings (Grigoli et al., 2018; Koskela & Viren, 1983). The private pension may increase total saving if it is not offset by decreases in other forms of household saving (Hubbard, 1986; Munnell, 1976). Because of population aging, many countries worldwide are switching the retirement pension from a defined benefit (which provides a prespecified income stream to the pension recipient at retirement and until death) to defined contribution (where individuals contribute and manage the pension fund by themselves) (see Poterba et al., 2007, for a comparison).

Government Interventions

Governments have an incentive to encourage household saving in that it alleviates the need for future social support. Tax exemption and tax deferral on retirement, education, and long-term saving plans are strategies usually used by governments. Empirical studies find that financial incentives, including tax benefit, matching contribution, and deposition reward, can effectively boost household savings in corresponding accounts (Agarwal, Chomsisengphet, et al., 2017; Carroll & Summers, 1987; Duflo et al., 2006).

Consumption

Early Theories on Household Consumption

Although household consumption is usually considered together with saving decisions in economic models, the consumption function was formalized later than the saving function, because consumption is defined as the passive residual part of income after saving in classical economic models. Early economists believed that consumers could individually pursue decisions to meet their own demand, which would naturally steer resources toward healthy economic developments. In this sense, the market was under the control of the “invisible hand” and government intervention was unnecessary (Smith, 1776). Say et al. (1803) assured that supply would always create its own demand; hence, the self-regulating market could adjust itself, and unemployment would be temporary and minor. However, Say’s law was challenged by repeated economic downturns and sustained unemployment during the Great Depression in the 1930s. Keynes then advocated for a mixed economy, where the free market is allowed to function, but government intervention is also necessary to avoid chronic unemployment and economic instability (Keynes, 1927).

Keynes observed that instead of being a passive residual of investment, hence changing with the interest rate, consumption increases with households’ absolute disposable income (Keynes, 1936). Moreover, given that lower-income households will spend a higher proportion of their income, households with higher income have higher APC (average propensity to consume, defined as the ratio of total consumption to total disposable income). In this sense, the Keynesian consumption function would expect the APC to decline continuously as the national income grows over time, which is inconsistent with the empirical observation that although the APC decreases in the short run, it remains stable in the long run (Kuznets, 1946).

In response to the Kuznets (consumption) puzzle, Friedman (1957) proposed the permanent income hypothesis (PIH), which adds a forward dimension to consumption theory that can reconcile the observed differences between short-run and long-run APC. The Keynesian model doesn’t distinguish between transient and permanent income changes, but the PIH assumes that only changes in permanent income—the expected income to be received over a long period of time into the future—will alter household consumption. Similarly, consumption is also divided into permanent and transitory components, and the permanent consumption is a constant proportion of permanent income; hence the MPC (marginal propensity to consume, defined as the ratio of change in consumption to change in disposable income) is equal to APC out of permanent income, while the MPC out of transitory income is 0.

Another response to the consumption puzzle is the life-cycle hypothesis (Modigliani & Brumberg, 1954). This model assumes individuals have complete knowledge about the consumption needs in the future and seek to maintain the same level of consumption throughout their life. With free borrowing and no liquidity constraint, individuals can realize the constant consumption level by saving, taking on debt, or liquidating assets. The amount available for consumption at each time point is the sum of the beginning net worth and present value of income minus the present value of the planned bequest. In this sense, a one-time increase in income will have the same effect as a one-time increase in wealth, but individuals will spread the income increase over multiple periods, generating a persistent impact. In the short run, wealth is unlikely to change proportionally with income, so wealth remains the same given an increase in income, rendering a lower APC from a higher income. In the long run, wealth and income increase in a proportional manner, which gives a constant APC.

To isolate optimizing behaviors, early economic models usually make the simplified assumption that households are rational and homogeneous, the homo economicus defined by Pareto (2014). However in reality, households are often indecisive and heterogeneous, and face various constraints and biases. Later models and empirical studies add these significant features of consumer preferences and market imperfections to enhance the fitting.

Augmented Models and Empirical Studies on Household Consumption

Liquidity Constraints

Both PIH and life-cycle hypothesis assume that households smooth consumption. Hence, consumption will not vary unless long-term expectation on income has changed. However, empirical studies document that consumption responds excessively to even anticipated increase in income (Mastrobuoni & Weinberg, 2009; Parker, 1999; Souleles, 1999, 2002; Stephens, 2003). Liquidity constraint can potentially explain this phenomenon. When households do not have easy access to credit, it would be hard for them to borrow as desired to smooth consumption, leading to excessive consumption response to income increase (Jappelli & Pagano, 1989). Liquidity constraint may also lead to precautionary saving where constrained individuals want to preserve certain liquidity to buffer future negative events (Deaton, 1991). The liquidity constraint also affects the timing of consumption: In response to an anticipated income increase, households respond significantly at the announcement if without liquidity constraint, whereas the liquidity constraint forces households to delay consumption increase until receipt of the increased income.

Studies document evidence consistent with liquidity constraints. Stephens (2006) found a significant increase in nondurables and food consumption upon anticipated paycheck arrival for liquidity-constrained households. (Liquidity-constrained) U.S. households are also found to significantly increase spending in response to anticipated temporary tax rebates or stimulus (Agarwal et al., 2007; Agarwal, Marwell, et al., 2017; Johnson et al., 2006; Shapiro & Slemrod, 1995, 2003, 2009). Other forms of anticipated income increase, such as vehicle loan payments and random stimulus payments, are also found to significantly boost consumption, especially among liquidity-constrained households (Parker, 2017; Stephens, 2008).

One caveat of the liquidity constraint is that it cannot explain the significant decline of consumption in response to anticipated income decrease, which can be simply smoothed by saving. Shea (1995) finds that household consumption does not significantly change in response to anticipated wage increase but significantly declines in response to anticipated wage decrease, which is inconsistent with liquidity constraint explanation. A series of studies documents significant consumption drop after retirement, which is also inconsistent with the PIH or life-cycle hypothesis, even with the existence of liquidity constraint (Banks et al., 1998; Bernheim et al., 2001). But this can be rationalized by either that the retirement was not anticipated (Haider & Stephens, 2007) or that individuals have more time after retirement to search cheaper goods or for home production (Aguiar & Hurst, 2005, 2007a, 2007b; Li et al., 2015).

A few papers also investigated consumption response to unanticipated income shocks. Unanticipated income shocks can be either temporary or transitory, and theories predict a significant almost one-on-one consumption change to the announcement of an unanticipated (positive or negative) permanent shock; whereas Friedman (1957) argued that the MPC out of transitory income shock should be small. Studies do document significant MPC out of (the announcement and receipt of) unanticipated income, and most of them can be explained by PIH or life-cycle hypothesis with liquidity constraint (Agarwal & Qian, 2014; Bodkin, 1959; Browning & Crossley; 2001; Di Maggio, et al., 2017). Baker and Yannelis (2017) found that during the 2013 government shutdown, the government employees who received an unanticipated negative shock to liquidity significantly changed their consumption, and the excessive sensitivity was largely driven by liquidity-constrained individuals. Gelman et al. (2020) also documented a decrease in consumption during the government shutdown, but they found that individuals smooth consumption by borrowing.

Uncertainties

Income uncertainties induce prudent consumers to defer consumption and increase precautionary saving (Carroll, 1997, 2001; Carroll et al., 1992; Carroll & Kimball, 1996). Households with higher income uncertainty tend to be more pessimistic and have more precautionary behaviors, and studies find that income uncertainty is higher among younger households (Ben-David et al., 2018; Dominitz & Manski, 2006). Based on a series of lab tests, Fuster et al. (2021) found that the responses to losses are larger than gains, which can be explained by the precautionary saving model with sufficient liquidity constrained households. Similar responses are documented by Christelis et al. (2019).

Uncertainty about health and longevity may lead to either unintended bequest (Yaari, 1965) or exhaustion of accumulated resources, and the latter calls for social insurance programs. Hubbard et al. (1995) found low-income households choose not to save in order to remain eligible for social insurance programs. Financial products such as annuities and reverse mortgages can help households to hedge longevity risk, but they seem to be unpopular (Johnson et al., 2004). Various explanations are provided for the low adoption of annuities, including the preference for lump-sum payment, inability to annualize at favorable rates, bequest motives, alternative resources of annuity income, and inertia and lack of financial sophistication (Benartzi et al., 2011; Brown & Poterba, 2000; Lockwood, 2018).

Bequest Motives

Bequest motive can also potentially account for unexplained changes in observed consumption. People leave unintended bequests when they die accidentally, but bequest motives usually refer to voluntary bequests that individuals intentionally plan to leave to family. Evidence on the prevalence of voluntary bequests is mixed. Although Kotlikoff and Summers (1981) estimated that 80% of U.S. wealth accumulation is due to intergenerational transfers, Modigliani (1988) suggested this fraction is just 20%. Bernheim et al. (1985) found that families with higher bequeathable wealth and more children have higher visitation rates, suggesting that parents can strategically use bequests to influence children’s decisions.

Non-Separability

The intertemporal non-separability suggests that the consumption in one period affects the person’s consumption in the next period, which can arise from reasons including habits, cue theory, durability, and homeownership. With habit formation, the marginal utility derived from present consumption also depends on past consumption; hence, the habits reduce the variability of consumption across time. However, studies find that the impact of habit formation on nondurable consumption is limited (Dynan, 2000; Meghir & Weber, 1996; Rhee, 2004). Cue theory suggests that individuals receive additional utility of consumption from paring of a cue; for example, the expectation of a positive feeling of pleasure after a drink increases the marginal utility of drinking. Laibson (2001) suggested that small changes in cues can give rise to a large marginal utility of consumption. Durable goods such as housing, appliances, furniture, vehicles, books, and electronic equipment, once consumed, can affect household consumption in multiple subsequent periods. Adams et al. (2009) and Aaronson et al. (2012) documented that a small income increase induces spending (i.e., the small down payment) for vehicles among lower-income households. Similarly, housing is a large durable good requiring a high down payment. Households usually finance housing consumption through a mortgage. Consistent with the PIH, Gerardi et al. (2010) documented that, since the 1980s, housing spending predicts households’ future income, indicating a small imperfectness of the mortgage market.

The intra-temporal non-separability suggests that the consumption of one type of goods affects the utility of other types of goods within the same period. One example is the non-separability between work and leisure: people tend to work more if the price of leisure (i.e., wage) is high (Blundell et al., 2016; Heckman, 1974). Another example is home production, meaning that households produce goods and services for their own consumption. Home production can potentially explain the consumption decline after retirement (Aguiar & Hurst, 2005), but Been et al. (2020) suggested that home production is unlikely to smooth wealth shocks because a low proportion of goods can be substituted by home production. Moreover, if heterogeneous opinions from household members are considered, more complicated models will be needed (Chiappori & Mazzocco, 2017).

Behavioral Factors

Instead of being fully informed rational decision agents, real households are usually subject to human fallibilities such as bounded rationality, mental accounting, hyperbolic preferences, present bias, and peer influence.

Bounded rationality suggests that consumers tend to smooth consumption when the expected changes in income are large, but less so when the changes are small because the disutility from not smoothing consumption is small (Browning & Collado, 2001). Mental accounting suggests that individuals are more willing to spend certain sources of income or assets than others (Shefrin & Thaler, 1988). Researchers find that households have different MPCs for different assets, and the MPC is higher for windfall gains compared with regular income (Thaler, 1990; Tversky & Kahneman, 1981), higher for liquid assets versus real estate wealth (Levin, 1998), and higher for dividend income compared with capital gains (Baker et al., 2007; Di Maggio et al., 2020).

Another behavioral factor is hyperbolic preferences, where individuals are impatient and lack self-control, which prevents them from carrying out good intentions. Hyperbolic discounting can explain anomalies such as under-saving and a sharp consumption decline at retirement (Laibson, 1998). To avoid bias, individuals may accumulate illiquid assets as a form of commitment; pensions, certificates of deposits, and housing are golden egg properties that provide substantial benefits in the long run that are impossible to realize immediately (Laibson, 1997). Present bias increases the desire for instant gratification, which may drive households to borrow from expensive sources such as credit cards (Meier & Sprenger, 2010). With peer influences, the utility of an individual’s consumption will be affected by the consumption of peers. Peer effects are documented among various types of goods, including automobile, food, movie tickets, housing, warranty claims (Agarwal, Chomsisengphet, et al., 2019; Bailey et al., 2018; Cai et al., 2009; Gilchrist & Sands, 2016; Grinblatt et al., 2008; Kuhn et al., 2011; Moretti, 2011); and various definitions of peers based on racial groups, neighborhoods, and working networks (Agarwal et al., 2021b; Charles et al., 2009; De Giorgi et al., 2020; Kuhn et al., 2011).

Payment for Consumption

Pain of Payment

Making the payment is painful, but the pain of paying can be cushioned by consumption (Prelec & Loewenstein, 1998). Loosely coupled payment and consumption are less painful (Soman & Gourville, 2001), but this may not be welfare enhancing: DellaVigna and Malmendier (2006) showed that the cost per visit is higher for consumers paying monthly gym membership. Researchers also find that the more transparent the payment outflow, the greater the perceived pain and aversion to spending (Prelec & Loewenstein, 1998; Soman, 2003).

Payment Instruments

Payment can be made through different instruments, including cash, check, debit cards, and credit cards. Koulayev et al. (2016) found that consumers prefer different payment modes when purchasing different types of goods. Demographic characteristics such as wealth, education, and age also affect the choice of payment instrument (Klee, 2008; Koulayev et al., 2016). Bachas et al. (2021) showed that debit cards reduce current consumption and increase household savings because of lower transaction costs and monitoring costs of accessing money. Credit cards, which became a widely accepted payment instrument in the 21st century, are found to facilitate spending (Hirschman, 1979). The status symbol for a certain credit card may induce the demand from high-income customers to signal their wealth (Bursztyn et al., 2018). As an unbiased measure that captures the disaggregated sales and real-time customer demand for a firm, credit card spending is predictive of the firm’s subsequent stock returns (Agarwal et al., 2021a).

New Payment Technologies

New payment technologies are usually cheaper and better for consumers (Bachas et al., 2018), but the adoption can be slow and uneven. From the demand side, the elderly may be less willing to adopt new technology because of a lower present value of the benefits (Yang & Ching, 2014). From the supply side, the widespread adoption of the new technology from one part of banks (merchants) can spill over to other banks (merchants) in the system, generating a positive clustering (Gowrisankaran & Stavins, 2004). The demand and supply sides can also feed back to each other. Agarwal, Qian, et al. (2019) documented that the introduction of a new quick response (QR) code payment technology increases sales for small and entrepreneurial merchants not only through the QR code payment but also through the bank card payment, suggesting that the new technology boosts the customer demand by reducing transaction frictions.

Investments

Equity Market Participation

Increased consumption creates more demand for income, so an examination of household investment behavior naturally follows the discussion of consumption and saving decisions. The neoclassical economic model implies nonzero participation of all individuals in the risky asset market. In particular, the consumption portfolio choice model by Merton (1969) predicted that all rational investors hold the market portfolio, where the portfolio weight of each security is proportional to its market capitalization. The conclusion holds regardless of individual risk preference or level of wealth. However, past literature documented a substantial discrepancy between what the theory predicts and how households allocate their assets. A large fraction of households are holding very little risky asset in their portfolio. Such observed gap is usually termed as “stockholding puzzle” or “nonparticipation puzzle.”

Fixed Costs

Over the past two decades, a large strand of literature has been devoted to trying to explain the puzzle, or more broadly what drives the decision to participate or quit. Households may choose to stay away from the equity market despite of the positive equity premium for a variety of reasons. For example, zero stockholding could arise simply because of the presence of fixed costs, which rational investors weigh against the potential benefits earned by investing in the equity market Such costs could exist in monetary terms, such as the expenses incurred for account opening, costs paid to trade, or fees for the portfolio managers or financial advisors (Vissing-Jørgensen & Attanasio, 2003). More, investing in the stock market may also impose indirect costs, as individuals are likely to devote more time and efforts in learning by investing (i.e., stock picking, seeking financial advice). This leads to one implication of fixed participation costs: Participation is positively correlated with investor’s risk preference because more risk-tolerant investors are willing to allocate a larger share of their wealth to risky assets (Haliassos & Bertaut, 1995). Direct validation of the fixed-cost theory per se is impossible because the estimate of total participation costs is difficult. Therefore, extant studies rest on testing its implication and providing consistent evidence. Guiso et al. (2003) documented that stock ownership rate is positively correlated with the level of household wealth and education in the context of European countries. However, the fixed participation cost hypothesis can hardly depict the full story, as it fails to rationalize the fact that a nontrivial fraction of wealthy households does not invest in the equity market.

Trust

The decision to invest in risky assets should depend on the investor’s prior perception of the risk. In other words, in order for the household to participate, the investor needs to exhibit a sufficient level of trust in the investment. Guiso et al. (2008) highlighted the fundamental role of trust in driving individual’s willingness to invest in the stock market. In general, the trust could reflect confidence in two dimensions. First, the decision to invest calls for trusting the objective feature of the financial system, including the quality of information sources and the enforcement of investor protection. Second, trust also reflects the investor’s belief about the subjective feature, namely, the fairness of the game or the probability of being cheated. As shown in the survey conducted in Guiso et al. (2008), trust indeed predicted investor stockholding decisions, and the result was robust to the inclusion of individual risk and ambiguity aversion, the latter of which indicates that trust and preferences play separate roles in one’s participation. The importance of trust is also confirmed in the participation decision of other financial assets. Trust can decrease the market participation reducing the subjective likelihood that people give to incidents of deception and thus mitigate contracting incompleteness and influences investor demand in active delegated management (Arrow, 1972; Gambetta, 1988). Massa et al. (2020) explored the potential role of trust in active management in the global mutual fund industry and found that trust is positively associated with fund activeness. Three features of the trust-based argument make it a compelling explanation. First, trust can account for limited participation even among the wealthy, as it does not vary much across wealth levels. Second, as a stable individual trait, it can explain the persistent reluctance or inclination to invest in risky assets. Third, trust varies systematically across nations and thus is powerful in rationalizing heterogeneity of stockholding across countries.

Other Factors

Stock market participation decision can also be driven by nonstandard preference. For example, recent economic and finance literature has started to recognize the role of social influence in household financial decision. Individuals are more willing to participate in the stock market if their peers are also stock market participants (Hong et al., 2004). In a rational model, such peer effect in stockholding arises because of both observational learning and “social utility,” namely, one’s utility is affected by another’s possession of the asset. Households may learn from their past trading experience, which can explain the limited stock market participation (Linnainmaa, 2011; Malmendier & Nagel, 2011). Stock market participation is also shown to correlate with socioeconomic status (Kuhnen & Miu, 2017), investor cognitive skills (e.g., Benjamin et al., 2013; Grinblatt et al., 2011), and financial literacy and education (Lusardi & Mitchell, 2007a, 2007b; Van Rooij et al., 2011).

Portfolio Choice

Theory

Once the investor decides to participate in a risky asset market, what stocks will they choose for their portfolio? It’s worthwhile to start the overview of normative models with the classic mean-variance analysis of Markowitz (1952), assuming the mean and variance of the portfolio return over a single time are all that matters to investors. The main implication of mean-variance paradigm is that all investors should hold risky assets in the same proportion. Consequently, investors differ only in the overall scale of their risky asset portfolio, not in the composition of that portfolio. This result is termed the “mutual fund theorem” (Tobin, 1958). Similar insights can be obtained in the utility framework where investors are assumed to have utility defined over wealth at the end of the period. In the presence of riskless and non-tradeable human wealth, the optimal financial allocation strategy is slightly different, tilting toward risky financial assets. This is what motivates the emergence and popularity of the life-cycle fund in the 2010s.

Under-Diversification

The basic concept of modern financial theory is to hold a diversified portfolio because unsystematic risk is not compensated. Empirical research on household portfolio diversification aims to answer these basic questions: (a) Are households following the diversification principle? And (b) if so, what are the implications for their welfare? Odean (1999) exploited survey data for a representative sample of households that may suffer from measurement error, whereas others used a highly selected sample from one brokerage house with more detailed and accurate information. Calvet et al. (2007) made a significant advancement in overcoming the measurement error or restricted data limitations to characterize household portfolios, using administrative data covering all Swedish resident households. These authors documented that Swedish households on average hold a highly undiversified portfolio of very few stocks. The lack of diversification can be extremely costly: The median household lost only 30 basis points of financial wealth relative to market performance (Calvet et al., 2007). Under-diversification is greater among younger, low-income, less-educated, and less-sophisticated investors and decreases with one’s financial literacy (Goetzmann & Kumar, 2008; Guiso & Jappelli, 2008; Polkovnichenko, 2005).

Information and Preference

The observed portfolio under-diversification was not necessarily interpreted as a financial mistake. The choice can be rationalized when investors have certain information advantages that they can utilize to outperform the market. This notion was supported by a few studies in the literature (Korniotis & Kumar, 2013; Van Nieuwerburgh & Veldkamp, 2010). For instance, households with concentrated portfolios outperform those with diversified portfolios, conditional on similar investment skills (Ivković et al., 2008). The information advantage could be applicable to specific contexts. There is evidence that investors are likely to invest in companies that are geographically proximate, or where they are employed (e.g., Graham et al., 2009; Grinblatt & Keloharju, 2001). The tendency to stick with local stocks and employers’ stocks, also broadly termed as “local bias,” motivates investors to optimally tilt their portfolios away from the market portfolio, to earn higher risk-adjusted return, while leading to under-diversification. However, professional and geographical proximity is also consistent with a preference-based explanation: Investors prefer certain financial assets regardless of the payoff. Preference can take many other forms as well, including ambiguity aversion (Boyle et al., 2012) and taste for positively skewed payoffs (Mitton & Vorkink, 2007), which is discussed next in “Trading—Behavioral Views.”

Trading—Behavioral Views

Research in behavior finance tried to modify the classical expected utility framework by factoring psychological realism into the traditional models of investment. This method produces two improvements: First, it recognizes that how investors form and update individual beliefs is not fully rational (i.e., following Bayes’ rule), and considers a more realistic process. One important finding along this line of research is that individuals are overconfident and trade excessively. The concept of overconfidence helps to explain many puzzling aspects of asset pricing, most notably high trading volume. In a simple economy with many investors, being overconfident can lead to underestimating the precision of others’ signals to one’s own. Therefore, substantial disagreement exists among investors, generating high trading volume. The overconfidence hypothesis is also validated on the empirical front. Given that men are generally more overconfident than women (Lundeberg et al., 1994), Barber and Odean (2000) found that men trade more and earn lower returns because of the transaction costs they incur. Other research studies exploring survey-based measures of overconfidence documented similar facts (Grinblatt & Keloharju, 2009). Overconfidence also helps to explain other stylized facts about asset prices, such as excess volatility and return predictability (Daniel et al., 1998).

Behavioral finance also takes account of more realistic assumptions about individual preferences. Given the limitations of the expected utility framework in accurately describing individual decision, prospect theory is proposed to capture a variety of experimental evidence (Tversky & Kahneman, 1992). One implication of prospect theory in the trading context is the well-documented disposition effect, namely, investors have the tendency to sell assets that have increased in value, or “winners,” while keeping those that have decreased in value, the “losers.” As for the determinant of disposition effect, some studies attributed it to an explanation alternative to prospect theory. Barberis and Xiong (2012) argued that realized gains and losses play a pivotal role in forming the disposition effect. Disposition effect is one of the most replicated investor behaviors among both individual and institutional investors, in both the lab and field data, as well as both in the United States and internationally, and throughout the year except for December (Ben-David & Hirshleifer, 2012; Ivković & Weisbenner, 2009; Kaustia, 2010; Shefrin & Statman, 1985; Hirshleifer, 2015 provides a recent survey). It is also observed in other asset classes (e.g., Li et al., 2021). The tendency to exhibit disposition effect is shown to be correlated with IQ, past trading experience, wealth, and financial sophistication (Calvet et al., 2009; Dhar & Zhu, 2006).

Since the 2010s, a growing literature has suggested that measuring disposition effects at the security level is too restrictive, and investors may conduct comparisons within their portfolio. Consistent with this notion, studies found that individual investors evaluate gains and losses at the portfolio level rather than at the security level (i.e., An et al., 2019; Ghosh et al., 2022; Hartzmark, 2015).

Housing

Homeownership

Housing wealth emerges as a significant component of household net worth. This is true across educational levels and across all ethnic groups (Lusardi & Mitchell, 2007b). The decision to buy or rent is faced by every individual as they make their housing choice. Beracha and Johnson (2012) developed a framework for the decision between renting and owning and highlighted the advantages and disadvantages of owning (against renting). Owning a house entitles the household to receive periodic cash flow in terms of rent income, relative to renters. At the same time, homeowners are subject to fluctuations in house prices and rentals (asset price and income risk). The asset price risk is binding when the household decides to liquidate the house. Following this logic, it’s not surprising to expect that the decision to rent or buy depends crucially on house price fluctuation.

Agarwal, Hu, et al. (2016) documented a strong correlation between homeownership rates and average house price growth, in particular for younger households. They further attributed the influence of house price growth to two different channels: (a) the liquidity constraint channel—a household will postpone buying a first home because house prices are expected to rise significantly, and consumers may be forced to cut back on their spending to cover the down payment; and (b) the expectation channel—the household will buy a first home earlier, as they expect house prices to rise even faster in the future. The household’s perception of house price risk also matters for the decision, and households often take an extrapolation approach to infer future risk from past price dynamics (Adelino et al., 2018).

The buy-or-rent decision can lead to different implications. Housing serves as a saving commitment instrument, so homeowners are able to accumulate wealth at a faster speed than renters (Somerville et al., 2007). Owning a house also increases individual’s caring and willingness to participate in community activities. The higher level of commitment enhances the well-being of the local community, which rationalizes the government’s preferential policies for homeowners, including tax subsidies and mortgage interest deductions.

As a portfolio decision, homeownership should also matter for other household decisions. Individuals who invest a higher fraction of their wealth in real estate should invest less in risky assets (Cocco, 2005; Yao & Zhang, 2005). This partially explains homeowners’ limited stock market participation. As the initial down payment typically requires substantial savings, a large fraction of homeowners have a very low level of liquid assets and are hand-to-mouth consumers who exhibit an excessive response to positive income shock (Kaplan et al., 2014). The collapse in housing net worth negatively affects consumption (Mian et al., 2013). Home ownership can also affects the individual’s decision to supply labor, as the household often views home appreciation as a form of speculative income. Increases in house price raise shirking behavior and lower labor productivity (Gu et al., 2018).

Mortgage Demand

Mortgages play a crucial role in the household’s ability to buy homes, as property purchase is usually financed by borrowing from the bank. It is typically the single largest household liability and has high penetration in both developed and emerging markets (Badarinza et al., 2016, 2019). However, the size of the mortgage market varies substantially across countries. The heterogeneity can be driven by economic conditions, demographic structure, legal and regulatory frameworks, mortgage market institutions, and house price expectation (Bailey et al., 2019; Chiuri & Jappelli, 2003; Guiso & Jappelli, 2002; La Porta et al., 1998). Mortgage arrangements also exhibit significant cross-country variations. The United States and Germany rely primarily on fixed-rate mortgages (FRMs); Australia, the United Kingdom, and many southern European countries are virtually reliant on adjustable-rate mortgages (ARMs). Some other countries like Denmark, Sweden, the Netherlands, and India are sitting in between and vary substantially in the prevalence of ARMs versus FRMs over time (Gomes et al., 2021). The choice of an ARM versus an FRM could have important financial consequences for financial stability and monetary policy transmission.

Mortgage Refinancing

Most households must borrow money to buy a home. Mortgage refinancing is relevant for FRM borrowers and allows them to benefit from lowering the cost of borrowing when interest rates decline. The simple rule of thumb adopted in the financial advising industry is that refinancing is optimal when one can recoup the closing costs of refinancing in reduced interest payments. Academic studies highlight the role of option value in refinancing and argue that optimal refinancing is essentially the solution to an optimal stopping problem (Agarwal et al., 2013). Households often fail to make optimal refinancing decisions and make errors of commission and omission. Refinancing mistakes vary with demographic characteristics: less educated and less wealthy households are less likely to refinance when interest rates decline (Andersen et al., 2015; Keys et al., 2016).

There is a growing interest in uncovering the relative importance of factors that may impede optimal refinancing. Some research attributes it to the constraints (i.e., poor credit history and low equity position) that consumers may face, as the borrower has to be qualified for a new mortgage (Archer et al., 1996; Campbell, 2006). Others attribute the refinance failure to behavioral factors: For example, high-income and wealthy consumers find it optimal to refinance only if the incentive is sufficiently high (Andersen et al., 2015).

Mortgage Default

Mortgage default occurs when the borrower is unable to keep up with mortgage payments Van Rooij, and the lender decides to foreclose. The occurrence of default crucially depends on the individual borrower’s risk characteristics. Borrowers with worse credit scores, higher loan-to-value, negative equity factors, and illiquidity ratios are more likely to default (i.e., Agarwal, Ambrose, et al., 2012). Mortgage default can also arise when the economic condition deteriorates and consumers experience negative income shock (i.e., job loss). Another strand of literature emphasizes the rise in defaults arising from the supply side. In particular, many factors, including rapid expansion of credit, improper rating of mortgage-backed securities, and lax screening efforts potentially contribute to the massive mortgage defaults and foreclosures during the recent recession (Agarwal, Benmelech, et al., 2012; Mian & Sufi, 2009; Ospina & Uhlig, 2018).

Borrowing

Reasons and Effects of Household Borrowing

Households borrow mainly for four reasons: smooth consumption, smooth temporary fluctuations in income (Guerreri & Lorenzoni, 2017), investments (Robb & Robinson, 2014), and behavioral biases. All the money borrowed that will be repaid later compromises the household debt, which can be secured (i.e., lenders have the right to repossess the collateral by default, such as is the case with mortgages and vehicle loans) or unsecured (i.e., no collateral provided, such as with personal loans, student loans, credit card debt, and payday loans). An adequate level of household debt is beneficial as it improves living standards and enhances economic growth (Beck & Levine, 2004), whereas an overly high household debt becomes a source of financial vulnerability that impedes economic growth (Eggertsson & Krugman, 2012; Gourinchas & Obstfeld, 2012; Korinek & Simsek, 2016; Mian et al., 2017).

The permanent income hypothesis (PIH) suggests that households consume according to permanent income in the future, and therefore individuals who anticipate an increase in future income will borrow to expedite consumption (Hall, 1978). Access to credit can replace the “rainy day” savings and cash holdings that households held to buffer unexpected events (Carroll et al., 1992; Deaton, 1991). Younger households that anticipate future income to grow tend to borrow more (Blundell et al., 1994), and a greater income or wealth inequality may lead to rapid growth in household debt among the poor and the middle class as they seek to maintain or increase consumption when real earnings have stalled (Kumhof et al., 2015). In the event of sudden personal emergencies such as illness or job loss, households with liquidity constraints may resort to high-cost, short-term unsecured loans such as payday loans to facilitate spending (Morse, 2011). Researchers also found that households borrow more from credit cards to substitute debit account spending when facing uncertainty (Agarwal, Ghosh, et al., 2021; Baugh et al., 2017). Last but not least, behavioral biases such as present bias and optimism may also lead to overconsumption and overborrowing (Agarwal, Chomsisengphet, Meier, et al., 2020; Fuster et al., 2010; Meier & Sprenger, 2010).

The expansion of financial opportunities arising from financial innovations can also induce higher household indebtedness (Dick & Lehnert, 2010; Dynan, 2009; Mayer et al., 2009). It is widely argued that rapidly rising household debt led to the 2008 financial crisis (Mian & Sufi, 2011). With the U.S. banking deregulation in 2004, some consumers who were previously denied access were allowed to borrow, leading to increased willingness of financial markets (and consumers) to lend (and borrow) (Di Maggio & Kermani, 2017; Mian & Sufi, 2018); and the subsequent housing price decline led to a widespread default and collapse of the financial system. After the housing bust, financial institutions become more cautious by tightening the lending standards and requiring higher interest rates (Hall, 2011). The crisis caused further negative repercussions such as declined household consumption and higher unemployment (Mian et al., 2013). At the micro level, stress from loans can also affect one’s path in life, for example, career choice (Lise, 2013). Moreover, borrowers, especially those in poorer or developing economies, can fall into a long-lasting debt spiral when they are unable to serve high-interest loans (Karlan et al., 2019).

Efficiency of Household Debt

Does Borrowing Improve Household Welfare?

Under liquidity constraints, households will be better off if they can borrow to facilitate consumption (Carroll et al., 1992; Deaton, 1991; Ludvigson, 1999). High-cost borrowing can be beneficial to some households when the marginal utility of (a small amount of) borrowing is high and cheaper source of liquidity is unavailable. However, researchers also find that payday loan customers are associated with worse credit performance, healthcare, job performance, and financial literacy (Bertrand & Morse, 2011; Carrell & Zinman, 2014; Melzer, 2011).

Do Borrowers Make Optimal Decisions?

Evidence shows that borrowers do not always choose deals that minimize their costs, even with the help of professional advisors (Agarwal et al., 2011). One reason for the inability is the cost of searching in order to understand the terms and conditions of the financial products (Hortaçsu & Syverson, 2004); in this case, adequate education, along with searching effort, will help in decision making (Bertrand & Morse, 2011; Campbell, Jackson, et al., 2011). Some borrowers suffer from present bias and hence are overly impatient in the short run. They tend to extract equity for consumption (Ausubel, 1991; Meier & Sprenger, 2010) or borrow high-interest loans and fail to repay later, incurring unnecessary overdue costs and penalty fees (Agarwal, Skiba, et al., 2009; Kuchler & Pagel, 2021; Stango & Zinman, 2009).

On the other hand, the credit providers may intentionally steer customers toward more expensive products (Agarwal, Amromin, et al., 2016). For example, they use sales tactics that just highlight the benefits but hide the costs of a product (Agarwal, Chomsisengphet, et al., 2015; Agarwal, Song, et al., 2020; Gabaix & Laibson, 2006; Gurun et al., 2016; Stango & Zinman, 2011, 2014), or use junk mails to influence customer choice (Agarwal & Ambrose, 2018). A small incentive such as a 1% cash back reward can significantly boost spending and debt accumulation (Agarwal et al., 2010). Framing bias suggests that how the loan application is designed can also influence one’s decision (Abraham et al., 2020; Pallais, 2015). Agarwal, Qian, et al. (2020) found that banks may offer higher credit lines and debt forgiveness to bureaucrats as hidden bribes.

The complexity of financial products also makes some individuals, especially the unsophisticated ones, make non-optimal choices. One example is the refinance of the fixed-rate mortgage, which allows the borrower to get extra liquidity by trading in the old mortgage for a new one when the interest rate declines. However, making the refinance decision requires both interest rate picking and correct timing, leading to a non-optimal choice of some households and a cross-subsidy of naive households to sophisticated households (Agarwal, Rosen, et al., 2016; Agarwal & Mazumder, 2013; Campbell, Jackson, et al., 2011). Shifting to an adjustable-rate mortgage may bring new issues because changes in monthly payments can have serious consequences for liquidity-constrained homeowners (Campbell & Cocco, 2003).

Besides lack of information, information overload may also lead to inefficient decisions. Bettinger et al. (2012) found that simplifying the student loan application process and providing direct help with the application can improve college access. Researchers have found that individuals tend to make credit card payments around the minimum payment level because of an anchoring effect (Kahneman & Tversky, 1974; Keys & Wang, 2019). Similarly, individuals may prefer to choose the default payment method even if it is suboptimal, which suggests that welfare can be improved by simply changing the default choice (Cox et al., 2020; Marx & Turner, 2018). Other motivations such as self-control or aversion toward income verification or carrying debt can also result in inefficient borrowing or repayment of student loans (Cadena & Keys, 2013; Field, 2009; Lochner et al., 2021).

For borrowers with multiple credit card debts, the optimal behavior for them is to make the minimum required payments on all the cards and repay the card debts with higher interest rates first. Some studies found that (high-income and highly educated) households can efficiently allocate their debt repayment (Becker & Shabani, 2010; Stango & Zinman, 2016). However, other researchers documented the opposite result: Most households do not follow the optimal repayment sequence, leading to a coexistance of higher financial costs and unused liquidity (Gathergood et al., 2019; Grubb, 2014; Ponce et al., 2017; Stango & Zinman, 2014). Gross and Souleles (2002a) recorded a credit card debt puzzle where households revolve credit card debt and hold liquid assets at the same time. One potential explanation is that certain expenditures such as mortgage and rent do not accept credit card payments (Telyukova, 2013). Another paradox is the coexistence of frequent card borrowing and voluntary retirement saving for the same individual, which can be consistent with the hyperbolic preference interpretation if the saving is in the form of an illiquid asset (Laibson et al., 2003).

Borrowers may default if they are unable to repay. Researchers have documented that unemployment and social factors may lead to credit card default (Deng et al., 2000; Gross & Souleles, 2002b; Agarwal & Liu, 2003); individuals may also strategically default if the financial benefit increases (Fay et al., 2002). However, default is not only costly for the defaulter but can also bring negative externality to peers (Campbell, Giglio, et al., 2011). In this sense, renegotiation will be a better choice. However, Agarwal, Amromin, et al. (2017) found that the policy incentivizing renegotiation of residential loans has limited effect. Some researchers have proposed that the agency problem in the securitized mortgage, frictions in renegotiation contract, and asymmetric information on self-cure and redefault risks lead to insufficient renegotiation (Adelino et al., 2013; Maturana, 2017; Piskorski et al., 2010). However, the non-capability of financial intermediaries also works negatively to avoid foreclosures. Even after default and personal bankruptcy, the law that allows borrowers to keep their primary property and catch up on payments can significantly decrease the foreclosure rate (Dobbie & Song, 2015).

Studies show that households improve their decisions as they acquire more experience (Agarwal et al., 2008; Agarwal, Chomsisengphet, et al., 2015; Miravete, 2003), but when the memory fades, they can make mistakes again (Haselhuhn et al., 2012). Technologies that provide timely reminders to consumers may help them reduce unnecessary fees, but may also lead to an increase in spending and other types of fees (Carlin et al., 2017; Fernandes et al., 2014; Medina, 2021).

Does the Market Supply Adequate Credit?

There is mixed evidence on credit undersupply and oversupply in the market. The evidence supporting the credit undersupply view includes price dispersion in consumer credit, missing supply of credit for a certain interest rate range, asymmetric information between the borrower and financial institutions, the market power and switching costs between credit providers, and unwillingness of households to pay for advice (Carrell & Zinman, 2014; DellaVigna, 2009; Dobbie & Skiba, 2013; Einav et al., 2013; Knittel & Stango, 2003; Malkiel, 2013; Woodward & Hall, 2012). Nevertheless, there is also evidence consistent with the credit oversupply view, including the existence of advantages selection (where less risky borrowers borrow at unfavorable terms and are supplied by higher credit beyond what they require), the mass fire sale of collateral (where lenders force borrowers to sell the collaterals emergently to repay loans in the face of negative shock), slow recovery from negative shocks for high-leverage industries, and a cash-out refinancing of mortgage (De Meza & Webb, 2000; Hall, 2011; Khandani et al., 2013; Mian & Sufi, 2011, 2015).

FinTech

Digital Lending

The 2008 financial crisis triggered a collapse of public confidence in long-established and high-street banks and the traditional banking model. This created valuable opportunities for the development of financial technology (FinTech), which offered an alternative solution and transformed the sector dramatically. The new financial intermediaries, known as the shadow banking system, function in a similar way as traditional banks and credit unions, by providing maturity transformation service and facilitating the creation of credit. However, they do not face the normal regulatory oversight because they do not accept traditional deposits. The advantage with lower regulatory burdens has led to their rapid expansion after the financial crisis. Buchak et al. (2018) estimated that the market share of shadow banks in mortgage origination has nearly doubled, from roughly 30% in 2007 to 50% in 2015.

A notable advantage of FinTech is the ability to use big data to assess credit risk more intelligently and create a more diverse and stable credit sector. For example, monitoring mobile phone usage behavior can be alternative source of information about one’s creditworthiness and potentially reduce the cost of financing (Björkegren & Grissen, 2018). The new predictive model also includes sparse and detailed customer data such as unstructured data left by individuals through online accounts. Using one of the largest FinTech lending firms, CASHe, in India, Agarwal, Alok, et al. (2019) showed that incorporating digital footprint variables (i.e., logging into social media platforms) can introduce substantial improvement in the predictive power for defaults. Another study by Berg et al. (2020) documented similar patterns using comprehensive data from and e‑commerce firm in Germany.

FinTech lending has important implications for enhancing lending efficiency and financial inclusion, and potentially reducing consumer’s financial distress (i.e., Agarwal, He, et al., 2018; Keys et al., 2020). A technology-oriented approach allows FinTech lenders to process applications 20% quicker without increasing loan risk (Fuster et al., 2019). FinTech is also more capable of withstanding demand shocks; it helps expand credit access to the underbanked population ex ante; and it accommodates minority groups who are often discriminated against by traditional lenders (Bartlett et al., 2022).

Peer-to-Peer Lending

Peer-to-peer (P2P) lending, also referred to as “marketplace lending” or “crowdfunding,” has emerged and is rapidly gaining market share in consumer and small business lending since the Great Recession. The P2P lending approach is unique in that it directly matches both lenders and borrowers through an internet platform, without the need of an intermediary bank, to originate loans to people and enterprises. The lending platforms are designed to offer both borrowers and lenders a low-cost, standardized loan application procedure. Lending is heavily relied on screening and information production by investors (Vallee & Zeng, 2019). For instance, the leading P2P platform in United States, Prosper, allows the borrower to include voluntary and unverified information, which may favorably prejudice judgment and strongly influence the process of expectation formation (i.e., Duarte et al., 2012; Hassin & Trope, 2000).

Redefining the traditional intermediary role is also costly; that is, P2P lenders are facing even more severe problems of information asymmetry (Lin et al., 2013). The existence of severe information asymmetry within peer-to-peer lending can be partially resolved via screening through nonstandard, or “soft,” information. Iyer et al. (2016) quantified the scope and nature of conclusions drawn from various sources (hard and soft information) in screening P2P borrowers, and found that the accuracy of default prediction by lenders is 45% higher. The inclusion of social network or third-party guarantees at P2P lending are also shown to be effective in alleviating the information asymmetries, and result in lending efficiency (Agarwal, Li, et al., 2015; Jin & Freedman, 2014; also, Agarwal & Zhang, 2020, provides a review).

Robo-Advisors

Financial illiteracy is one of the obstacles that prevents households from participating in the risky asset market. FinTech has automated investment advice at low costs and with low account minimums, which facilitates consumers’ access to professional finance advice (Abraham et al., 2019). This digital financial advice is based on mathematical rules or algorithms that do not require a human advisor, thus mitigating behavioral bias. Examining the introduction of a wealth management, robo-advising technology in India, D’Acunto et al. (2019) confirmed the benefits of adopting robo-advisors and found that heterogeneity varies with the investor’s portfolio choices ex ante.

Further Reading

  • Agarwal, S., Ang, S. H., & Sing, T. F. (2018). Kiasunomics©: Stories of Singaporean economic behaviours. World Scientific.
  • Agarwal, S., Ang, S. H., & Sing, T. F. (2020). Kiasunomics© 2: Economic insights for everyday life. World Scientific.
  • Agarwal, S., Qian, W., & Tan, R. (2020). Household finance. Springer.
  • Tufano, P. (2009). Consumer finance. Annual Review of Financial Economics, 1(1), 227–247.
  • Zinman, J. (2015). Household debt: Facts, puzzles, theories, and policies. Annual Review of Economics, 7(1), 251–276.

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