The Effects of 401(K) Participation on the Wealth Distribution: An Instrumental Quantile Regression Analysis

By Victor Chernozhukov, Christian Hansen. 2004

213 citations recorded by the source corpus.

Subfields: Accounting, Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “We use data from the 1991 US Survey of Income and Program Participation (Chernozhukov et al. (2018b)), using sample selection and variable construction as in Abadie (2003) and Chernozhukov and Hansen (2004).”
Double/Debiased Machine Learning for Treatment and Causal Parameters (2016, methodology): “onfounding eects can adequately be controlled for by a small number of variables chosen ex ante by the researcher. 24 DOUBLE MACHINE LEARNING In the example in this paper, we use the same data as in [29]. We use net nancial assets - dened as the sum of IRA balances, 401(k) balances, checking account”
A quantile regression approach for estimating panel data models using instrumental variables (2009, methodology): “Our approach extends the recent work of Chernozhukov and Hansen (2008) on instrumental variables by allowing for “fixed effects” as introduced in Koenker (2004).”
An IV Model of Quantile Treatment Effects (2002, background): “Hausman and Sidak (2002), Januszewski (2002), and Chernozhukov and Hansen (2001a, 2004) present economic applications of the model and method proposed here.”
Instrumental Variable Quantile Regression (2004, methodology): “Chernozhukov and Hansen (2004) use the framework of the IVQR model to examine the effects of participating in a 401(k) plan on an individual’s accumulated wealth.”
Instrumental quantile regression inference for structural and treatment effect models (2006, background): “Chernozhukov and Hansen (2004) estimate the distributional impact of 401(k) participation on assets.”
Program evaluation and causal inference with high-dimensional data (2013, methodology): “on the accumulated nancial assets of those with low desire to save but a much larger impact on those with stronger preferences for saving. It is interesting that these results are similar to those in Chernozhukov and Hansen (2004) despite allowing for a much richer set of controls. Links to the lite”
Program evaluation with high-dimensional data (2013, methodology): “To overcome the endogeneity of 401(k) participation, Abadie (2003) and Chernozhukov and Hansen (2004) adopt the strategy detailed in Poterba, Venti, and Wise (1994; 1995; 1996; 2001) and Benjamin (2003), who used data from the 1991 Survey of Income and Program Participation and argue that…”
Double/debiased machine learning for treatment and structural parameters (2017, unknown): “In the example in this paper, we use the same data as in Chernozhukov and Hansen (2004).”
Do remittances improve income inequality? An instrumental variable quantile analysis of the Kenyan case (2016, methodology): “To achieve this, we use the instrumental variable quantile approach suggested by Chernozhukov and Hansen (2005, 2008) and implemented for the analysis of inequality and wage dispersion by Harding and Lamarche (2009).”
Nonparametric Instrumental Variables Estimation of a Quantile Regression Model (2006, background): “Chernozhukov and Hansen (2004) and Januszewski (2002) use such models in economic applications.”
Corporate Governance, Incentives, and Tax Avoidance (2015, methodology): “- 14 - Quantile regression has also been applied in finance and economics to assess the effects of 401(K) participation on wealth (Chernozhukov and Hansen, 2004); the determinants of house prices (Zietz et al., 2008); the determinants of gender wage differences (Garcia et al., 2001); the effect of…”
Information Disclosure Policies: Evidence from the Electricity Industry (2007, methodology): “For instrumental variable QREG, we use the implementation by Chernozhukov and Hansen (2004) for estimation and inference.”
Instrumental variable quantile regression: A robust inference approach (2008, methodology): “It has already been used in empirical applications, e.g. Chernozhukov and Hansen (2004), Hausman and Sidak (2004), Januszewski (2004), Chevalier and Feinstein (2005), Kondylis (2005), Maynard and Qiu (2005), Melly (2005), Autor, Houseman, and Pekkala (2006), Ginsburgh and Prieto-Rodriguez (2006),…”
Threshold Regression with Endogeneity (2017, unknown): “The same approach is used by Abadie (2003) and Chernozhukov and Hansen (2004) in estimating local average treatment effects (LATEs) and the quantile treatment effects, respectively.”