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Risky Decisions: Revealing Economic Behaviour

Periodic Reporting for period 4 - RDRECON (Risky Decisions: Revealing Economic Behaviour)

Reporting period: 2019-11-01 to 2020-12-31

Our goal was to gain insights into how personal and market experiences affect individuals and households' financial decisions. Do individuals learn from past mistakes of their own or others? To this end, RDRECON has enhanced our understanding of personal, household, and market experiences causal effect on financial decisions, thus providing helpful insights for policymakers.

The research team looked into how individuals and households are affected by financial market experiences. Initially, we looked at how the dramatic financial events under the latest financial crisis in 2008-2010 affected individuals' financial investment decisions. We then examine how the loss of investments through bank defaults and individuals' investments in these banks leads to subsequent behavior changes. Continuing in this path, we then looked into how financing and refinancing of housing are affected by different households and individual characteristics - how individuals' choices affect savings and optimality of financial decisions – for example, how this differs by gender. We use these findings the financial, macroeconomic policies can transfer differently to different demographics groups because they behave sub-optimal.

The research team then turned more into the core of the search project – to look at how core preferences and beliefs of individuals and households channels through to how they make financial decisions. We developed and estimated types of choice models of inertia and refinancing choices to examine how. A highly non-linear model, On this type of model, we build and estimate a loss aversion model in the housing market. This extended model estimates the importance of loss aversion in the propensity to sell houses, a deep parameter of high importance for macroeconomic policy and labor market mobility.
The research team initially used the housing assets of individuals to look at individuals and households' financial decisions. We look at housing assets because properties are the largest investments that households make, which predominantly impact households and individuals' opportunities. From this line of research, we looked at gender gaps in bargaining abilities in the article "Gender Differences in Negotiation: Evidence from Real Estate Transactions," forthcoming in The Economic Journal; the in the suboptimality of different types of households when they refinance their housing assets in the article "Sources of inaction in household finance: Evidence from the Danish mortgage market", American Economic Review. 2020, 110(10), 3184-3230; the effect on households savings and pension by the forced saving commitments in mortgage repayments in the article "The Effect of Savings Commitments on Asset, Debt, and Retirement Decisions: Evidence from Mortgage Run-offs in Danish Registry Data," forthcoming in Journal of Money, Credit and Banking; and the effect of financial distress and financial sophistication on the ability to make financial decision in the article " Forced Sales and House Prices: Evidence from Estate Sales due to Sudden Death," Management Science, 63(1), 2017, 201-202.
Prof. Kasper Meisner Nielsen and I then looked at how individuals and households are affected by financial market experiences. Initially, we looked at how the dramatic financial events under the latest financial crisis in 2008-2010 affected individuals' financial investment decisions. We then examine how the loss of investments through bank defaults and individuals' investments in these banks leads to subsequent behaviour changes. We investigated this in the article "Once Bitten, Twice Shy: Do Personal Experiences or Wealth Changes Affect Risk-Taking," Journal of Financial Economics 132 (3), 97-117. June 2019.
We then turned more into the core of the search project – to look at how core preferences of individuals and households channels through to how they make financial decisions. In "Sources of inaction in household finance: Evidence from the Danish mortgage market," American Economic Review. 2020, 110(10), 3184-3230, we developed and estimated types of choice models of inertia and refinancing choices. A highly non-linear model, designed to explain heterogeneity in refinancing choices, takes months to estimate the standard hardware (i.e. servers) at Statistic Denmark. On this type of model we build a model of loss aversion in the housing market in "Reference Dependence in the Housing Market," 2019. This extended model estimates the importance of loss aversion in the propensity to sell houses, a deep parameter of high importance for macroeconomic policy.
The research conducted under the past ERC starting grant is part of an ongoing research agenda that exploits administrative register data on households' financial decisions in Denmark –research agenda our team will keep exploiting. With already four top publications (2x AER, 1 JFE, 1MS) and two excellent journal articles (EJ and JMCB), 1 top revise and resubmit at AER, and 2-3 top economic publications coming out of this project, I very pleased with our performance. Additionally the grant allowed spillovers to two other field publications, which I expect more publications to come from in the future from this grant. We disseminated this at several conferences and workshops.
Through the experimental planning phase, both Statistics Denmark, other researchers in Denmark and outside Denmark, as well as the public has greatly learned from the possibilities given by this methodology. The latter makes it possible to learn something about groups of interest, that otherwise would be hard to identify in a random sample approach. To this end, many by-product projects using this methodology are now underway to start. We additionally developed econometric methods as well as developed the structure of computational power to uncover deep parameters of interest at the individual level within conventional computing time from the project's output. Traditionally models are build and executed for samples over a few thousand observations. As our research projects modeled behavior over several million individuals over long periods, we needed substantial efficiency improvements of conventional statistical coding to handle algorithms' complexity. We did this building on the research in the project. At the beginning of this year, 2021, we obtained additional funding for greater computational power to continue our voyage and research into how deep preferences parameters form individual and household financial choices and outcomes.
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