The rules of thumb offered by financial advisors regarding how much to hold in liquid reserves vary widely and usually imply far greater sums than low-income ho
The Co-holding Puzzle: New Evidence from Transaction-Level Data
Using detailed and highly disaggregated data on household finances, we examine the<br>tendency of consumers to “co-hold” savings and debt simultaneously. The di
Household Finance
Household financial decisions are complex, interdependent, and heterogeneous, and central to the functioning of the financial system. We present an overview of the rapidly expanding literature on household finance (with some important exceptions) and suggest directions for future research. We begin with the theory and empirics of asset market participation and asset allocation over the life cycle. We then discuss household choices in insurance markets, trading behavior, decisions on retirement saving, and financial choices by retirees. We survey research on liabilities, including mortgage choice, refinancing, and default, and household behavior in unsecured credit markets, including credit cards and payday lending. We then connect the household to its social environment, including peer effects, cultural and hereditary factors, intra-household financial decision-making, financial literacy, cognition, and educational interventions. We also discuss literature on the provision and consumption of financial advice.
Demand for emergency savings is higher for low-income households, but so is the cost of shocks
This paper examines how the precautionary motive varies with income. I first develop a theoretical benchmark of how we would expect precaution to vary with income starting from a basic version of the buffer-stock model. Emergency savings provide a way for households to smooth over shocks and so give insight into the precautionary motive. Using data from the Survey of Consumer Finances in the USA, I show that as income declines, the desired emergency savings relative to income increase, suggesting that low-income households are more precautionary. Observable differences, such as income uncertainty, do not explain the rise. Instead, I propose and estimate a model with a minimum subsistence level and unexpected expenses. The model implies that low-income households are increasingly exposed to shocks, explaining the increase in precaution. Supporting the approach, I show that expenses on repairs are a larger fraction of the spending of low-income households.
Does Saving Cause Borrowing? Implications for the Coholding Puzzle
ABSTRACT Using an experiment in which 3.1 million bank customers were encouraged to save, we explore the mechanisms behind coholding liquid savings and credit card debt. Theoretically, we show that the joint responses of spending, saving, and borrowing to the nudge differ across economic models of coholding. Using machine learning techniques, we find that the most responsive individuals reduce spending and increase savings by 4.9% (206 USD PPP per month) while their credit card debt remains unchanged. These individuals' marginal responses to the nudge are consistent with our model of coholding for the purpose of self‐ or partner‐control.
Cash versus Debit Card: The Role of Budget Control
Due to the financial crisis, an increasing number of households face financial problems. This may lead to an increasing need for monitoring spending and budgets. We demonstrate that both cash and the debit card are perceived as helpful in this respect. We show that, on average, consumers responsible for financial decision making within a household find cash and the debit card equally helpful for monitoring their household finances. Individuals differ in major respects, however. In particular, low earners and the liquidity‐constrained prefer cash as a budgeting tool. Finally, we present evidence that at an aggregated level, such preferences strongly affect consumer payment behavior. These findings suggest that the substitution of cash by cards may slow down because of the financial crisis.
The Coholding Puzzle: New Evidence from Transaction-Level Data
Abstract Why do individuals pay debt interest when they could use their savings to pay down the debt? We explore why individuals “cohold” debt and savings using detailed and highly disaggregated daily-level data on household finances. We find that coholding mostly occurs in short spells within the month and the level of coholding is typically modest. Periods of coholding are not associated with shocks at the individual level. We show that mental accounting has a role to play in explaining coholding, in particular how individuals allocate different categories of expenditure to accounts in credit and debit.
Household Financial Transaction Data
The growth of the availability and use of detailed household financial transaction micro data has dramatically expanded the ability of researchers to understand both household decision making and aggregate economic fluctuations across a wide range of fields. This class of transaction data is derived from a myriad of sources, including financial institutions, FinTech apps, and payment intermediaries. We review how these detailed data have been utilized in finance and economics research and analyze both their benefits and limitations as compared to more traditional measures of income, spending, and wealth. Finally, we discuss the future potential of this flexible class of data in firm-focused research, real-time policy analysis, and macro statistics.
Household Debt: Facts, Puzzles, Theories, and Policies
Borrowing decisions affect most households, with large stakes and implications for research subfields as varied as macroeconomics and industrial organization. I review theoretical and empirical work on household debt: its prevalence, level, growth, and composition, as well as various measures of consumer choice and market (in)efficiency, elasticities, and prices, including new evidence on how borrowing heterogeneity affects the distribution of the opportunity cost of consumption. I also discuss opportunities and challenges in policy evaluation. A key takeaway is that puzzles abound, and I highlight numerous avenues for further research.
What Would You Do with $500? Spending Responses to Gains, Losses, News, and Loans
Abstract We use survey questions about spending in hypothetical scenarios to investigate features of propensities to consume that are useful for distinguishing between consumption theories. We find that (1) responses to unanticipated gains are vastly heterogeneous (either zero or substantially positive); (2) responses increase in the size of the gain, driven by the extensive margin of spending adjustments; (3) responses to losses are much larger and more widespread than responses to gains; and (4) even those with large responses to gains do not respond to news about future gains. These four findings suggest that limited access to disposable resources, and frictions in adjusting consumption, are important determinants of consumption behaviour. We also find that (5) households do not respond to the offer of a one-year interest-free loan, suggesting that this is not a consequence of short-term credit constraints; and (6) people do cut spending in response to news about future losses, suggesting that neither is this a consequence of myopia. A calibrated precautionary savings model with utility costs of changing consumption, and a sufficient fraction of low-wealth households, can account for these features of propensities to consume on both the extensive and intensive margins.
Does Saving Cause Borrowing? Implications for the Co-Holding Puzzle
Using an experiment in which 3.1 million bank customers were encouraged to save, we explore the mechanisms behind co-holding liquid savings and credit card debt. Theoretically, we first show that the joint responses of spending, saving, and borrowing to the nudge differ for different economic models of co-holding. Using machine learning techniques, we then find that the most responsive individuals reduce spending and increase their savings by 4.9% (206 USD PPP per month) while their credit card debt remains unchanged. For them, the marginal responses to the nudge are consistent with our model of co-holding for the purpose of self- or partner control.
Liquidity versus Wealth in Household Debt Obligations: Evidence from Housing Policy in the Great Recession
We exploit variation in mortgage modifications to disentangle the impact of reducing long-term obligations with no change in short-term payments ("wealth"), and reducing short-term payments with no change in long-term obligations ("liquidity"). Using regression discontinuity and difference-in-differences research designs with administrative data measuring default and consumption, we find that principal reductions that increase wealth without affecting liquidity have no effect, while maturity extensions that increase only liquidity have large effects. This suggests that liquidity drives default and consumption decisions for borrowers in our sample and that distressed debt restructurings can be redesigned with substantial gains to borrowers, lenders, and taxpayers.
Exponential Growth Bias and Household Finance
ABSTRACT Exponential growth bias is the pervasive tendency to linearize exponential functions when assessing them intuitively. We show that exponential growth bias can explain two stylized facts in household finance: the tendency to underestimate an interest rate given other loan terms, and the tendency to underestimate a future value given other investment terms. Bias matters empirically: More‐biased households borrow more, save less, favor shorter maturities, and use and benefit more from financial advice, conditional on a rich set of household characteristics. There is little evidence that our measure of exponential growth bias merely proxies for broader financial sophistication.
Asymmetric Consumption Smoothing
Analyzing account-level data from an account aggregator, we find that households increase consumption when they receive expected tax refunds, as if they face liquidity constraints. However, these same households smooth consumption when making payments in other years, primarily by transferring funds among liquid accounts. Even households carrying credit card debt smooth consumption when making payments, and even highly liquid households spend out of refunds. This behavior is inconsistent with pure liquidity constraints or hand-to-mouth behavior and is most consistent with a mental accounting life-cycle model.
Co-holding behaviour: unlocking the puzzle
This article seeks to explain why households decide to simultaneously hold both credit and savings products. Beyond the arguments of ignorance or behavioural biases commonly used in the literature,...
Earmarking and Partitioning: Increasing Saving by Low-Income Households
This research examines the effects of earmarking money on savings by low-income consumers. In particular, the authors test two interventions that are designed to enhance the effects of earmarking: (1) using a visual reminder of the savings goal and (2) dividing the earmarked money into two parts. Consistent with prior research suggesting that partitioning increases self-control, people save more when earmarked money is partitioned into two accounts than when it is pooled into one account. In addition, the presence of the visual reminder increases the savings rate. The authors conclude with implications for consumer welfare and directions for further research.
The Self-Constrained Hand-to-Mouth
Abstract Many studies have shown that consumption responds to the arrival of predictable income (excess sensitivity). This paper uses a buffer stock model of consumption to understand what causes excess sensitivity and to test which parameterization is consistent with empirical excess sensitivity estimates. Using high-frequency granular data from a personal finance app, I find that while liquidity constraints are a proximate cause, preferences are the ultimate cause of excess sensitivity. Furthermore, it finds that for feasible parameters, a quasi-hyperbolic version of the model is more consistent with the level of excess sensitivity relative to a standard exponential model.
