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Article
Publication date: 1 April 1950

J. Girard and M. Delière

THE Flight Testing Department of the Société Nationale de Constructions Aéronautiques du Sud‐Ouest (S.N.C.A.S.O.) has, in collaboration with the Société Delord, developed a new…

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Abstract

THE Flight Testing Department of the Société Nationale de Constructions Aéronautiques du Sud‐Ouest (S.N.C.A.S.O.) has, in collaboration with the Société Delord, developed a new method of measuring and recording the variations in the electrical resistance of resistance type extensometers, known as strain‐gauges, in their various applications.

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Aircraft Engineering and Aerospace Technology, vol. 22 no. 4
Type: Research Article
ISSN: 0002-2667

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Publication date: 25 February 2016

Bhashkar Mazumder

Ideal estimates of the intergenerational elasticity (IGE) in income require a large panel of income data covering the entire working lifetimes for two generations. Previous…

Abstract

Ideal estimates of the intergenerational elasticity (IGE) in income require a large panel of income data covering the entire working lifetimes for two generations. Previous studies have demonstrated that using short panels and covering only certain portions of the life cycle can lead to considerable bias. I address these biases by using the PSID and constructing long time averages centered at age 40 in both generations. I find that the IGE in family income in the United States is likely greater than 0.6 suggesting a relatively low rate of intergenerational mobility in the United States. I find similar sized estimates for the IGE in labor income. These estimates support the prior findings of Mazumder (2005a, b) and are also similar to comparable estimates reported by Mitnik et al. (2015). In contrast, a recent influential study by Chetty, Hendren, Kline, Saez (2014) using tax data that begins in 1996 estimates the IGE in family income for the United States to be just 0.344 implying a much higher rate of intergenerational mobility. I demonstrate that despite the seeming advantages of extremely large samples of administrative tax data, the age structure, and limited panel dimension of the data used by Chetty et al. leads to considerable downward bias in estimating the IGE. I further demonstrate that the sensitivity checks in Chetty et al. regarding the age at which children’s income is measured, and the length of the time average of parent income used to estimate the IGE suffer from biases due to these data limitations. There are also concerns that tax data, unlike survey data, may not adequately reflect all sources of family income. Estimates of the rank–rank slope, Chetty et al.’s preferred estimator, are more robust to the limitations of the tax data but are also downward biased and modestly overstate mobility. However, Chetty et al.’s main findings of sizable geographic differences within the US in rank mobility are unlikely to be affected by these biases. I conclude that researchers should continue to use both the IGE and rank-based measures depending on their preferred concept of mobility. It is also important for researchers to have adequate coverage of key portions of the life cycle and to consider the possible drawbacks of using administrative data.

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Inequality: Causes and Consequences
Type: Book
ISBN: 978-1-78560-810-0

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