The 2019 CEPR European Workshop on Household Finance: summary

The 2019 CEPR European Workshop on Household Finance

May 10, 2019

A Summary

By Vimal Balasubramaniam

The 2019 edition of the CEPR European Workshop on Household Finance, held with the support of the Think Forward Initiative and the Arne Ryde Foundation, took place on 10 and 11 May in Lund, Sweden. This summary describes the main themes emerging from the papers presented at the conference.

On 10-11 May 2019, Lund University hosted the 2019 edition of the European Workshop on Household Finance organized by the CEPR Network on Household Finance. The Workshop was sponsored by the Arne Ryde Foundation, Swedish House of Finance, Think Forward Initiative, London Business School, and the PhD program at EDHEC Business School.

The Network runs the European Workshop on Household Finance in the spring of each year since 2016, alongside the European Conference that traces its origins to 2010. Preceding each year’s conference and workshop, the Think Forward Initiative and CEPR organise an event that includes a panel discussion around issues of topical interest. This edition’s event was organised by Stefania Albanesi and discussed fairness in consumer credit markets. The link to information on the most recent event can be found here. This summary presents the main themes that emerged from papers presented at the conference.

Household Wealth: Distribution, Inequality and Dispersion

The extent of inequality and dispersion in household wealth has become one of the defining problems of the world today. Standard life-cycle models of saving suggest that differences in time-discounting behaviour can play an important role in the endogenous determination of a household’s position in the wealth distribution. Exploring this, using high-quality Danish administrative data on wealth combined with experimental data with elicited time discounting, Epper, Fehr, Fehr-Duda, Kreiner, Lassen, and Rasmussen (2019) show that individuals with relatively low time-discounting are persistently better positioned in the wealth distribution and this relationship persists after accounting for traditional explanations of wealth inequality such as education, risk aversion, school grades, income, credit constraints, initial wealth and parental wealth.

An additional defining feature of the modern economy is automation. Exploring whether robots increase wealth dispersion in an economy, Jansson and Karabulut (2019) show that households who are more exposed to robots at work accumulate less wealth and witness downward mobility in the wealth distribution. Jansson and Karabulut also argue that this downward mobility is not only due to differences in labour income and savings rate, but also due to adverse effects on human capital.

The way households manage their wealth may also be a contributing factor for an uneven distribution of wealth. Sakong (2019) sheds light on this by documenting that poorer households consistently buy risky assets in booms (when expected returns are low) and sell after a bust (when expected returns are high). The dispersion in expected returns of about 60 basis points a year is one of the channels that cause portfolio returns to be increasing in wealth.

While other papers in the workshop focused on explaining wealth inequality and distribution, Bian, Lou, and Shi (2019) explore the redistributive role of financial bubbles and crashes and examine the direction of this redistribution. Using data from the Chinese stock market, they argue that the ultra-wealthy increase market exposures and tilt towards high beta stocks in the early stage of a bubble. While the ultra-wealthy exhibit better timing skills and reduce market exposures shortly after the market peak, relatively poor investors do not. They document a net transfer of over 200 billion RMB (26 billion Euros) over this 18-month period from the poor to the ultra-wealthy.

Individual Investor Behaviour: Attention, Noise and Probability Weighting

Individual investor attention to their portfolio and the market may have a significant bearing on well-established insights on the effect of aggregate investor attention on ownership, liquidity, return, correlation, and volatility of stocks. Exploring this gap in the academic literature, Arnold, Pelster, Subrahmanyam (2019) investigate how individual investor attention affects their trading behaviour and risk taking. Using a unique dataset from a brokerage service, they establish a causal link between standardized push messages sent by the brokerage service and individual trading behaviour. Push messages on a specific stock leads to increased long and short trades of that stock, whereas push messages that trigger attention to the portfolio improve investors’ portfolio diversification and induce investors to buy stocks with higher idiosyncratic risk.

The process of how individual investors learn from experience has been central to finance research in the recent years. Accumulating evidence suggests that investors are influenced by both the signal and noise components of their experience. Using a new research design with a natural experiment in India where investors participate in allocation lotteries for IPO stocks, Anagol, Balasubramaniam and Ramadorai (2019) show that randomized IPO gains cause winning investors to substantially increase portfolio trading-volume in non-IPO stocks relative to lottery losers. They also show investors who have received multiple past IPO allocations exhibit smaller responses, suggestive of a learning/selection process that moderates responses to noise.

Individuals violate the tenets of the workhorse theory of expected utility for low probability events – for instance, simultaneously buying insurance and lottery tickets and so on. To reconcile such seemingly anomalous behaviours with respect to individual portfolio choice, Dimmock, Kouwenberg, Mitchell and Peijnenburg (2019) elicit individual probability weighting preferences in a survey and their portfolio allocations in the equity market. They find that a one standard deviation increase in their measure of probability weighting behaviour implies a 12.7 percentage point increase in portfolio allocation to individual stocks.

Mortgage Market: Costs and Choice

A mortgage arguably is one of the most important financial choice households make in their lifetime. With a granular dataset of lending transaction reports, detailed product information and credit files from the UK, Iscenko (2019) asks whether each observed borrower may have been eligible for another product with the same features as their mortgage, but at a strictly lower price. Iscenko finds that one in three consumers made sub-optimal choices thereby paying 550 GBP more per year on average as a result.

While making mortgage choices, households are faced with several components of fees paid in order to complete the transaction. Using a comprehensive dataset on issued and offered mortgages in the UK, Liu (2019) documents that borrowers are less cost-sensitive to products with fees than without, and lenders differ substantially in the fees they charge. To isolate the supply-side pricing behaviour that exploits this borrower preference, Liu shows that lenders increase fees but not interest rates in response to firm-specific shocks to funding costs.

Household Debt, Response to Unemployment Shocks and Retirement Savings

It is well documented that many households simultaneously hold high-interest credit and substantial low-interest liquid assets. Vihriala (2019) documents three new observations regarding this puzzling behaviour: (a) households persistently belong to the puzzle group at the monthly frequency; (b) With an offer of new cheap liquidity only a few exit the puzzle group; and (c) The prevalence and persistence of puzzle behaviour is significantly lower at the individual than at the household level. In view of these findings, Vihriala argues that theories that rely on liquidity and precautionary borrowing concerns are insufficient and that intra-household optimization influences the probability of accepting the offer of new cheap liquidity.

The risk of unemployment is one of the major economic risks faced by households, yet its link to financial behavior has not been heavily researched. Using multiple high-frequency datasets covering the entire Danish population, Andersen, Jensen, Johannesen, Kreiner, Leth-Petersen, and Sherida (2019) document how households respond to unemployment shocks. They find that there is almost no change in spousal labour supply, a significant increase in the use of interest only and adjustable rate mortgage products with little impact on total monthly payments, no increase in mortgage debt, but a moderate increase in non-collateralized debt and a significant depletion of liquid assets. The largest margin of adjustment is on household spending, which drops by 6 percent on impact and remains at that lower level of a two-year period following job loss.

Household retirement saving mechanisms across the world are gradually moving away from defined benefit pension schemes to defined contribution schemes. However, it is likely that households with limited financial literacy, time-inconsistent preferences, and behavioural biases that depart from rational choices may end up with inadequate savings for retirement. Using data on more than 300,000 workers in the US enrolled in a defined contribution pension plan, Gomes, Hoyem, Hu, and Ravina (2019) evaluate whether they are likely to have enough wealth to finance an optimal retirement consumption path. In their baseline result, the authors document that close to three quarters of the workers in sample are not saving enough for retirement. The median individual has more than 40% probability of having to decrease consumption after age 65. With policy simulations from a model, the authors argue that modest increases in saving rates could improve retirement outcomes by sizable amounts for a large proportion of the population. However, for a significant fraction of workers, only drastic measures may lead to avoiding large reductions in their retirement consumption.