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Ideal estimates of the intergenerational elasticity (IGE) in income require a large panel of income data covering the entire working lifetimes for two generations…
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.
Purpose – This chapter aims at proposing a methodology for evaluating long-term income distributions according to the equality of opportunity principle.Approach – We refer…
Purpose – This chapter aims at proposing a methodology for evaluating long-term income distributions according to the equality of opportunity principle.
Approach – We refer to the concept that there is equality of opportunity if the value of the set of opportunities is the same for all individuals, regardless of their circumstances. This approach partitions the population into types, that is, groups of individuals with the same set of circumstances. The type-specific outcome distribution is interpreted as the opportunity set of individuals with the same circumstances. We propose partial and complete rankings on long-term type-specific distributions. Accordingly, these rankings capture inequality between types, and are neutral to inequality within types.
Findings – We show the relationship between long-run and short-run inequality of opportunity and that this relationship can be interpreted in terms of intragenerational mobility. We also show that mobility can act as an equalizer of opportunities when the accounting period is extended.
Originality – The contribution of this work is twofold. First, we develop a decomposition of some measures of long-term inequality of opportunity into measures of short-term inequality of opportunity, applied to distributions, which neutralize the effect of effort on individual income, and may be employed to explain eventual differences arising from an analysis based on the intertemporal context. Second, we propose an index to measure intragenerational mobility and show how it can be interpreted as long-term EOp. Our measure captures only that part of reranking due to the equalization of opportunities over time.
Purpose — In this chapter a case will be made for the importance of measuring well-being in transport mobility research. A number of well-being measures and determinants…
Purpose — In this chapter a case will be made for the importance of measuring well-being in transport mobility research. A number of well-being measures and determinants of well-being will be presented in reference to the current project. This chapter will then conclude with some practical recommendations for transport mobility researchers wishing to include well-being measures in their future studies.
Methodology — Measurement methods associated with previous transport mobility and well-being research will be critically examined so that strengths and limitations can be identified. The measurement approach to well-being adopted for the current project will be presented and associated challenges experienced by the research team will then be discussed.
Findings — A review of the extant transport mobility research which includes an assessment of well-being shows that it is not uncommon for unstandardised measures of well-being to be adopted. In addition, exploration of relationships between transport mobility and well-being are often undertaken without any consideration of potential moderating or mediating factors. More work is needed to advance our knowledge of the transport mobility and well-being relationship and the underlying mechanisms driving this relationship. Research also needs to focus on undertaking longitudinal studies which will enable causation to be established.
The economic reality of the 1990s in Europe forced the labor markets to become more flexible. Using a consistent comparative dataset for 14 countries, the European…
The economic reality of the 1990s in Europe forced the labor markets to become more flexible. Using a consistent comparative dataset for 14 countries, the European Community Household Panel (ECHP), we explore the degree of earnings mobility and inequality across Europe, and the role of labor market institutions in understanding the cross-national differences in earnings mobility. We study the degree of rank mobility and the degree of mobility as equalizer of long-term earnings. The country ranking in long-term earnings inequality is similar with the country ranking in annual inequality, which is a sign of limited long-term equalizing mobility within countries with higher levels of annual inequality. In long-term earnings inequality, Denmark renders the most mobile earnings distribution with the second highest equalizing effect. The only disequalizing mobility in a lifetime perspective is found in Portugal. With respect to the relationship between earnings mobility and earnings inequality, we find a significant negative association both in the short and the long run. Based on the rankings in long-term Fields mobility and long-term inequality, Denmark is expected to have the lowest lifetime earnings inequality in Europe, followed by Finland, Austria, and Belgium. The Mediterranean countries (Spain and Portugal) are expected to have the highest long-term inequality. With respect to the institutional factors that may be related to earnings mobility, we bring evidence that the deregulation in the labor and product markets, the degree of unionization, the degree of corporatism and the spending on ALMPs are positively associated with earnings mobility.
The aim of this article is to define a new kind of labor mobility called technological mobility, defined here as the different levels of technological change experienced…
The aim of this article is to define a new kind of labor mobility called technological mobility, defined here as the different levels of technological change experienced by workers as they change jobs over the course of their career. Technological mobility is viewed as a form of career mobility, and it is hypothesized that moving to jobs in higher‐tech industries might prove beneficial to workers' careers irrespective of the level of education or other measures of ability. Factors that determine upward or downward technological mobility are also investigated.
This hypothesis is tested using data from the NLSY79, a nationally representative survey of the United States, between the years 1988 and 2000. Determinants of upward and downward technological mobility are modeled using industry‐level data on technological mobility. Technological mobility is also regressed against wages to measure its impact on careers.
Gender, education and local economic conditions are found to have a significant effect on technological mobility, but the effect varies depending on the way technological intensity is measured. The results also demonstrate that workers who move to high‐tech industries are indeed rewarded with higher wages, even after controlling for education levels and other known factors.
Technological mobility as defined here is an original concept. It is shown to be an important component of overall career mobility. The article also provides an analysis of workers who are able to make the transition into higher‐tech jobs, which is a valuable addition to the research on technological change.
This chapter proposes tests for stochastic dominance in mobility based on the empirical likelihood ratio. Two views of mobility are considered, either based on measures of…
This chapter proposes tests for stochastic dominance in mobility based on the empirical likelihood ratio. Two views of mobility are considered, either based on measures of absolute mobility or based on transition matrices. First-order and second-order dominance conditions in mobility are first derived, followed by the derivation of statistical inferences techniques to test a null hypothesis of nondominance against an alternative of mobility dominance. An empirical analysis, based on the US Panel Study of Income Dynamics (PSID), is performed by comparing four income mobility periods ranging from 1970 to 1990.
This paper attempts to interpret the Bartholomew (1973) index of mobility in terms of a directional mobility index based on the one-step expected states of movement…
This paper attempts to interpret the Bartholomew (1973) index of mobility in terms of a directional mobility index based on the one-step expected states of movement corresponding to a specific state of transition matrix. A partial ordering of directional mobility of a general state of transition matrices, referred to as “upward mobility favoring sequential averaging (UMFSA),” is proposed using the algebraic equivalent of the generalized Lorenz ordering of expected states. When the underlying mobility depends on the initial distribution of the states, using a Bayesian approach, the indices are reexamined for a general class of matrices. This enables us to interpret the Prais (1955) and Bibby (1975) mobility index in this framework.