David Lander

Economics PhD candidate — Penn State

About me

I am a PhD candidate in the Department of Economics at the Pennsylvania State University, advised by Russell Cooper, and I will be on the job market in 2017-18.

My research areas are Macroeconomics, Household Finance, Labour Economics and Public Economics. In my dissertation I study consumer bankruptcy and government transfer policies using data from the Panel Study of Income Dynamics (PSID). My job market paper studies whether households filing for bankruptcy in the US exhibit strategic or nonstrategic behaviour.

Prior to my doctoral studies at Penn State, I received undergraduate (honours) and master's degrees in economics from the University of Melbourne.

I will be attending the ASSA Annual Meeting in Philadelphia, PA on January 5-7, 2018.

For further information, see my CV [pdf] and Research Statement [pdf].


Working papers

Strategic vs. Nonstrategic Household Behaviour: An Analysis of U.S. Bankruptcy Filings (job market paper) [pdf]

  • This paper studies whether households filing for bankruptcy are strategic, i.e., they file when the financial benefit of filing exceeds the cost, or nonstrategic, i.e., they file when unable to repay their debt. These types differ in their likelihood of filing, hence the distribution of types affects the availability and pricing of consumer credit. I extend a standard heterogeneous agent life-cycle model of household bankruptcy to include both strategic and nonstrategic agents, where these types differ only in terms of their filing decision rule. The proportion of each type is estimated using household-level data from the Panel Study of Income Dynamics. The model enables us to quantify both strategic and nonstrategic bankruptcy filings, and infer the magnitude of the alleged 'abuse' of Chapter 7 bankruptcy policy by strategic households. It also provides insights into previous findings in both the structural and reduced-form bankruptcy literature, by estimating the model parameters using simulated method of moments, with target moments taken from previous studies. The main result is that a structural model without nonstrategic agents has difficulty matching key moments, and it generates a significantly larger number of bankruptcy filings where the agents could repay. Consequently, it overstates the extent that Chapter 7 is being 'abused', and leads to predictions that exaggerate the effect of the 2005 Bankruptcy Code reforms implemented to curtail abusive filings.

On the Estimation of Income Processes Including Zero-Earnings Observations and Transfers [pdf — coming soon]

  • In economic applications regarding low-income or unemployed individuals, standard approaches for estimating labour-earnings processes are of limited use since they exclude zero-earnings observations and non-labour sources of earnings, such as government transfers. Modifications to these processes have been proposed, but it has not yet been demonstrated how accurately they approximate an underlying process comprised of labour earnings, unemployment and government transfers. Therefore, I undertake a Monte Carlo experiment to assess two approaches that incorporate either transfers or zero earnings into an exogenous income process. Both methods are evaluated compared to a benchmark life-cycle model with a consumption floor to represent transfers, and stochastic processes for earnings, unemployment, and retirement, estimated using the Panel Study of Income Dynamics. Including zero-earnings observations in the income process well approximates the benchmark model, but including transfers generally overestimates the transitory variance of disposable income, resulting in severe bias for parameter estimates of both the discount factor and coefficient of risk aversion. This provides support for separately modelling transfers from earnings, rather than including them in the income process.

Bayesian Assessment of Lorenz and Stochastic Dominance [pdf] — with David Gunawan, William Griffiths and Duangkamon Chotikapanich

  • We introduce a Bayesian approach for assessing Lorenz and stochastic dominance. For two income distributions, say X and Y, estimated via Markov chain Monte Carlo, we describe how to compute posterior probabilities for (i) X dominates Y, (ii) Y dominates X, and (iii) neither Y nor X is dominant. The proposed approach is applied to Indonesian income distributions using mixtures of gamma densities that ensure flexible modelling. Probability curves depicting the probability of dominance at each population proportion are used to explain changes in dominance probabilities over restricted ranges relevant for poverty orderings. They also explain some seemingly contradictory outcomes from the p-values of some sampling theory tests.

Work in progress

Modelling Household Income from Government Transfers over the Life Cycle

The Role of Education in the Household Bankruptcy Decision

Borrowing and Welfare Implications of an Income Floor — with Russell Cooper


Office address

Department of Economics

Pennsylvania State University

303 Kern Building

University Park, PA 16802


+1 (814) 380-8523

lander [at] psu.edu