On the relative rewards to immigration: a comparison of the relative labour market position of Indians in the USA,the UK and India

While most studies of the decision to immigrate focus on absolute income differences between countries, we argue that relative change in purchasing power or status, as captured by an individual’s ranking in the wage distribution, may also be another important determinant of the migration decision. Using data on Indian immigrants in the US and the UK matched to comparable data on individuals who remained in India, we show that the average Indian immigrant will experience a fall in their relative ranking in the wage distribution compared to the position similar individuals achieve by remaining in the origin country. The fall in relative rankings is larger for immigrants to the UK than to the US, and largest of all for those with intermediate skills.     

GMM with Weak Identification

This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak‐identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo studies. Confidence sets immune to weak identification are proposed. We use these results to inform an empirical investigation of various CCAPM specifications; the substantive conclusions reached differ from those obtained using conventional methods.     

Female Labor Supply,Human Capital,and Welfare Reform

We estimate a dynamic model of employment, human capital accumulation—including education, and savings for women in the United Kingdom, exploiting tax and benefit reforms, and use it to analyze the effects of welfare policy. We find substantial elasticities for labor supply and particularly for lone mothers. Returns to experience, which are important in determining the longer‐term effects of policy, increase with education, but experience mainly accumulates when in full‐time employment. Tax credits are welfare improving in the U.K., increase lone‐mother labor supply and marginally reduce educational attainment, but the employment effects do not extend beyond the period of eligibility. Marginal increases in tax credits improve welfare more than equally costly increases in income support or tax cuts.     

The paradox of parental participation and legal representation in ‘edge of care’ meetings

This paper assesses the nature of parental participation and legal representation in pre‐proceedings meetings in England and Wales. These are called when a local authority is considering care proceedings on a child. The parent(s) are invited to a meeting to discuss the concerns, and are entitled to attend with a lawyer. The paper draws on findings from a study of the process which included a file survey of over 200 cases, observations of 36 meetings and interviews with more than 90 key informants, including parents. The aim of the process is (usually) to reach an agreement to prevent the case going to court, but the families are usually well known to children's services, and have been through many meetings and agreements before. What then are the possibilities for parental participation and legal representation in the meetings? The study shows that they may help bring a greater degree of clarity to the local authority's proposals, but are not expected to challenge them. Paradoxically, they serve to reinforce the authority's position. The meetings can help divert cases, but it is important to be realistic about the chances of change in these often long‐standing ‘edge of care’ cases.     

Bayesian penalized smoothing approaches in models specified using differential equations with unknown error distributions

A full Bayesian approach based on ordinary differential equation (ODE)-penalized B-splines and penalized Gaussian mixture is proposed to jointly estimate ODE-parameters, state function and error distribution from the observation of some state functions involved in systems of affine differential equations. Simulations inspired by pharmacokinetic (PK) studies show that the proposed method provides comparable results to the method based on the standard ODE-penalized B-spline approach (i.e. with the Gaussian error distribution assumption) and outperforms the standard ODE-penalized B-splines when the distribution is not Gaussian. This methodology is illustrated on a PK data set.     

Non‐parametric Estimation of Extreme Risk Measures from Conditional Heavy‐tailed Distributions

In this paper, we introduce a new risk measure, the so‐called conditional tail moment. It is defined as the moment of order a ≥ 0 of the loss distribution above the upper α‐quantile where α ∈ (0,1). Estimating the conditional tail moment permits us to estimate all risk measures based on conditional moments such as conditional tail expectation, conditional value at risk or conditional tail variance. Here, we focus on the estimation of these risk measures in case of extreme losses (where α ↓0 is no longer fixed). It is moreover assumed that the loss distribution is heavy tailed and depends on a covariate. The estimation method thus combines non‐parametric kernel methods with extreme‐value statistics. The asymptotic distribution of the estimators is established, and their finite‐sample behaviour is illustrated both on simulated data and on a real data set of daily rainfalls.     

Method of Moments Estimation in Linear Regression with Errors in both Variables

Recently, in this journal, there has been revised attention on estimating the parameters of the errors in variables, linear structural model. For example, O’Driscoll and Ramirez (2011) used a geometric approach to give insight into the performance of various slope estimators for the linear structural model as introduced by the present author. This article aims to provide a unified method of moments approach for estimating the parameters in the linear structural model, concentrating attention on estimators using the higher moments, which to date has received only little attention in the literature.