Estimating continuous-time income models

While earning processes are commonly unobservable income flows which evolve in continuous time, observable income data are usually discrete, having been aggregated over time. We consider continuous-time earning processes, specifically (non-linearly) transformed Ornstein-Uhlenbeck processes, and the associated integrated, i.e. time aggregated process. Both processes are characterised, and we show that time aggregation alters important statistical properties. The parameters of the earning process are estimable by GMM, and the finite sample properties of the estimator are investigated. Our methods are applied to annual earnings data for the US. It is demonstrated that the model replicates well important features of the earnings distribution. Keywords; integrated non-linearly transformed ornstein-uhlenbeck process, temporal aggregation

Welfare measurement and measurement error

The approximate effects of measurement error on a variety of measures of inequality and poverty are derived. They are shown to depend on the measurement error variance and functionals of the error contaminated income distribution, but not on the form of the measurement error distribution, and to be accurate within a rich class of error free income distributions and measurement error distributions. The functionals of the error contaminated income distribution that approximate the measurement error induced distortions can be estimated. So it is possible to investigate the sensitivity of welfare measures to alternative amounts of measurement error and, when an estimate of the measurement error variance is available, to calculate corrected welfare measures. The methods are illustrated in an application using Indonesian household expenditure data.

Weak convergence to the t-distribution

We present a new limit theorem for random means: if the sample size is not deterministic but has a negative binomial or geometric distribution, the limit distribution of the normalised random mean is a t-distribution with degrees of freedom depending on the shape parameter of the negative binomial distribution. Thus the limit distribution exhibits exhibits heavy tails, whereas limit laws for random sums do not achieve this unless the summands have innite variance. The limit law may help explain several empirical regularities. We consider two such examples: rst, a simple model is used to explain why city size growth rates are approximately t-distributed. Second, a random averaging argument can account for the heavy tails of high-frequency returns. Our empirical investigations demonstrate that these predictions are borne out by the data.convergence, t-distribution, limit theorem

Illegal Migration, Wages, and Remittances: Semi-Parametric Estimation of Illegality Effects

We consider the issue of illegal migration from Mexico to the US, and examine whether the lack of legal status causally impacts on outcomes, specifically wages and remitting behavior. These outcomes are of particular interest given the extent of legal and illegal migration, and the resulting financial flows. We formalize this question and highlight the principal empirical problem using a potential outcome framework with endogenous selection. The selection bias is captured by a control function, which is estimated non-parametrically. The framework for remitting is extended to allow for endogenous regressors (e.g. wages). We propose a new re-parametrisation of the control function, which is linear in case of a normal error structure, and test linearity. Using Mexican Migration project data, we find considerable and robust illegality effects on wages, the penalty being about 12% in the 1980s and 22% in the 1990s. For the latter period, the selection bias is not created by a normal error structure; wrongly imposing normality overestimates the illegality effect on wages by 50%, while wrongly ignoring selection leads to a 50% underestimate. In contrast to these wage penalties, legal status appears to have mixed effects on remitting behavior.non-parametric estimation, control functions, selection, counterfactuals, illegality effects, illegal migration, intermediate outcomes, Mexican Migration Project

Subjective Income Expectations, Canonical Models and Income Risk

Expectations are central to behaviour. Despite the existence of subjective expectations data, the standard approach is to ignore these, to hypothecate a model of behaviour and to infer expectations from realisations. In the context of income models, we reveal the informational gain obtained from using both a canonical model and subjective expectations data. We propose a test for this informational gain, and illustrate our approach with an application to the problem of measuring income risk.subjective expectation data, canonical income models, income risk.

The Effect of Family Income during Childhood on Later-life Attainment: Evidence from Germany

We examine the impact of family income during childhood on the type of secondary school that German children attend, a good indicator of their lifetime socioeconomic attainment. By contrast with several US child outcome studies, we find that late-childhood income is a more important determinant of outcomes than early-childhood income, and income effects are not greater for poor households compared to rich households, other things equal. The income effects are small for native-born German children and non-existent for children from guestworker households. Income effects are also small relative to the impact of differences in parental educational qualifications or institutional factors related to the federal state of residence.child poverty, educational achievement, schooling, family background

Income Mobility: A Robust Approach (published in Income Inequality Measurement: From Theory to Practice, J Silber (ed, Dewenter: Kluver , 1999)

mobility measures; robustness; data contaminationmobility measures, robustness, data contamination

Measuring Income Mobility with Dirty Data (published in Ethnic and Racial Studies, 22(3), May 1999)

We examine the performance of measures of mobility when allowance is made for the possibility of data contamination. We find that 'single-stage' indices - those that are applied directly to a sample from a multivariate income distribution - usually prove to be non-robust in the face of contamination. However, 'two-stage' models of mobility - where the distribution is first 'discretised' into income intervals and then a transition matrix or other tool is applied - may be robust if the first stage is appropriately specified.Mobility measures, robustness, data contamination

The impact of labour market dynamics on the return-migration of immigrants

Using administrative panel data on the entire population of new labour immigrants to The Netherlands, we estimate the effects of individual labour market spells on immigration durations using the “timing-of-events” method. The model allows for correlated unobserved heterogeneity across migration, unemployment and employment processes. We find that unemployment spells increase return probabilities for all immigrant groups, while re-employment spells typically delay returns