Inferring Labor Income Risk and Partial Insurance from Economic Choices

Guvenen, Fatih; Smith, Adam

doi:10.21034/sr.485

Cited by 46 publications

(73 citation statements)

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“…The best SD model is found when the simulated auxiliary statistics match the empirical ones. While many different auxiliary models could be beneficial, the estimation would be more efficient if the auxiliary models were defined as precisely as possible; i.e. the auxiliary model is a good approximation of some aspects of f and g functions that are reflected in the estimated relationship (Guvenen and Smith, ). A more precise model reduces the variance of estimated regression coefficients (elements of S 1 , S 2 , …, S k ) and thus enables a reliable estimation with a smaller number of simulations, H .…”

Section: Indirect Inference Methodsmentioning

confidence: 99%

Estimating the parameters of system dynamics models using indirect inference

Hosseinichimeh

Rahmandad²,

Jalali³

2016

System Dynamics Review

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There is limited methodological guidance for estimating system dynamics models using datasets common to social sciences that include few data points over time for many units under analysis. Here, we introduce indirect inference, a simulation-based estimation method that can be applied to common datasets and is applicable to SD models that often include intractable likelihood functions. In this method, the model parameters are found by ensuring that simulated data from the model and available empirical data produce similar auxiliary statistics. The method requires few assumptions about the structure of the model and error generating processes and thus can be used in a variety of applications. We demonstrate the method in estimating an SD model of depression and rumination using a panel dataset. The overall results suggest that indirect inference can extend the application of SD models to new topics and leverage common panel datasets to provide unique insights.

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Section: Indirect Inference Methodsmentioning

confidence: 99%

Estimating the parameters of system dynamics models using indirect inference

Hosseinichimeh

Rahmandad²,

Jalali³

2016

System Dynamics Review

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“…In some cases, however, consumption data may be necessary to achieve identification. For example, Guvenen () and Guvenen and Smith () considered models in which individuals learn about their abilities so that individual ability differences are not fully reflected in earnings, at least at an early stage of the life cycle. Browning and Ejrnaes () and Hryshko () specified earnings processes in which different variance components are correlated.…”

Section: Relation To Literaturementioning

confidence: 99%

“… See, for example, Guvenen (), Primiceri and van Rens () and Guvenen and Smith (). Summary papers of the heterogeneous‐agents literature by Guvenen () and of the consumption literature by Meghir and Pistaferri () highlight the importance of earnings processes in structural modeling of life‐cycle choices. …”

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confidence: 99%

HIP, RIP, and the robustness of empirical earnings processes

Hoffmann

2019

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The dispersion of individual returns to experience, often referred to as heterogeneity of income profiles (HIP), is a key parameter in empirical human capital models, in studies of life-cycle income inequality, and in heterogeneous agent models of life-cycle labor market dynamics. It is commonly estimated from age variation in the covariance structure of earnings. In this study, I show that this approach is invalid and tends to deliver estimates of HIP that are biased upward. The reason is that any age variation in covariance structures can be rationalized by age-dependent heteroscedasticity in the distribution of earnings shocks. Once one models such age effects flexibly the remaining identifying variation for HIP is the shape of the tails of lag profiles. Credible estimation of HIP thus imposes strong demands on the data since one requires many earnings observations per individual and a low rate of sample attrition. To investigate empirically whether the bias in estimates of HIP from omitting age effects is quantitatively important, I thus rely on administrative data from Germany on quarterly earnings that follow workers from labor market entry until 27 years into their career. To strengthen external validity, I focus my analysis on an education group that displays a covariance structure with qualitatively similar properties like its North American counterpart. I find that a HIP model with age effects in transitory, persistent and permanent shocks fits the covariance structure almost perfectly and delivers small and insignificant estimates for the HIP component. In sharp contrast, once I estimate a standard HIP model without age-effects the estimated slope heterogeneity increases by a factor of thirteen and becomes highly significant, with a dramatic deterioration of model fit. I reach the same conclusions from estimating the two models on a different covariance structure and from conducting a Monte Carlo analysis, suggesting that my quantitative results are not an artifact of one particular sample. and seminar participants at the CEPA seminar at the University of Toronto, the UBC empirical micro lunch seminar, and the 2012 conference on the use of administrative data at the IAB for helpful suggestions, and Benedikt Hartmann, Daniela Hochfellner, Peter Jacobebbinghaus, and other staff members at the FDZ-IAB for their hospitality. Daniel Shack has provided excellent research assistance. The usual disclaimer applies.

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“…To obtain nonfinancial disposable income, we run a regression of the federal income tax variable (which is available in the PSID until 1990) on nonfinancial income and its square, and asset income and its square. This also follows Guvenen and Smith (2014). We use the estimated coefficients to predict post-tax labor income.…”

Section: A32 Tax Functionmentioning

confidence: 99%

“…Deaton and Paxson (1994) is the seminal empirical contribution. Storesletten et al (2004a), Guvenen (2007), Primiceri and Van Rens (2009), Huggett, Ventura and Yaron (2011) and Guvenen and Smith (2014) analyze lifetime inequality from the perspective of the standard, incomplete markets model as we do here. Within this literature, many of the consequences of richer earnings processes on consumption, savings and welfare in structural models are still unexplored, with few exceptions.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Household Earnings Dynamics, Self-Insurance, and Welfare

Nardi

Fella

Paz-Pardo

2019

Journal of the European Economic Association

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Earnings dynamics are much richer than typically assumed in macro models with heterogeneous agents. This holds for individual-pre-tax and household-post-tax earnings and across administrative and survey data. We estimate two alternative processes for household after-tax earnings and study their implications using a standard life-cycle model. Both processes feature a persistent and a transitory component, but although the first one is the canonical linear process with stationary shocks, the second one has substantially richer earnings dynamics, allowing for age-dependence of moments, non-normality, and nonlinearity in previous earnings and age. Allowing for richer earnings dynamics implies a substantially better fit of the evolution of cross-sectional consumption inequality over the life cycle and of the individual-level degree of consumption insurance against persistent earnings shocks. The richer earnings process implies lower welfare costs of earnings risk.

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Inferring Labor Income Risk and Partial Insurance from Economic Choices

Cited by 46 publications

References 0 publications

Estimating the parameters of system dynamics models using indirect inference

Estimating the parameters of system dynamics models using indirect inference

HIP, RIP, and the robustness of empirical earnings processes

Nonlinear Household Earnings Dynamics, Self-Insurance, and Welfare

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