# 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.

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.

For further information, see my CV [pdf].

## Research

### Working papers

**Strategic vs. Nonstrategic Household Behaviour: An Analysis of U.S. Bankruptcy Filings** [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.

**Estimating Life-Cycle Income Processes including Means-Tested Transfers **[pdf — coming soon]

- This paper studies whether it is appropriate to directly estimate a stochastic process for the sum of labour earnings and public transfers, i.e., disposable income. A Monte Carlo experiment is performed to evaluate how accurately this procedure approximates a simulated panel of income, generated by a life-cycle model with transfers in the form of an income floor. I show that means-tested transfer policies are not well-represented by a stationary stochastic process for income with the canonical ``transitory-and-persistent'' AR(1) structure, since means-testing generates age-dependence in the autocovariance structure of income. The consequence, in terms of economic inference, is demonstrated by using the method of simulated moments to estimate key behavioural parameters (i.e., the discount factor and coefficient of risk aversion), with target moments taken from the baseline model. While the bias of these parameter estimates, relative to the baseline model, is increasing in the income floor, the magnitude and direction of the bias depends on the choice of target moments.

**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

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

Modelling Household Income from Government Transfers over the Life Cycle

The Role of Education in the Household Bankruptcy Decision

## Contact

### Office address

Department of Economics

Pennsylvania State University

303 Kern Building

University Park, PA 16802

### Contact

+1 (814) 380-8523

lander [at] psu.edu