Mind the gap? Estimating the effects of postponing higher education

This paper estimates the effects on earnings of “gap years” between high school and university enrollment. The effect is estimated by means of standard earnings functions augmented to account for gap years and a rich set of control variables using administrative Swedish data. We find that postponement of higher education is associated with a persistent and non-trivial earnings penalty. The main source of the persistent penalty appears to be the loss of work experience after studies. Two years postponement reduces the present value of life time earnings by 40-50 percent of annual earnings at age 40.timing of education; schooling interruptions; work experience

Penalized estimation in large-scale generalized linear array models

Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension of the parameter vector. A new design matrix free algorithm is proposed for computing the penalized maximum likelihood estimate for GLAMs, which, in particular, handles nondifferentiable penalty functions. The proposed algorithm is implemented and available via the R package \verb+glamlasso+. It combines several ideas -- previously considered separately -- to obtain sparse estimates while at the same time efficiently exploiting the GLAM structure. In this paper the convergence of the algorithm is treated and the performance of its implementation is investigated and compared to that of \verb+glmnet+ on simulated as well as real data. It is shown that the computation time fo

Escolha adiada do parametro de penalização e do tamanho de passo em algoritmos de pontos interiores

Orientador: Clovis Perin FilhoTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação CientificaResumo: Nesse trabalho estudamos, no contexto de métodos de pontos interiores para programação linear, algumas possíveis vantagens de se adiar as escolhas do parâmetro de penalização e do tamanho de passo, que ocorrem tanto quando usamos o método de Newton para resolver o sistema de Karush-Kuhn- Thcker, como quando aplicamos um esquema preditor-corretor. Nós mostramos que, tanto para um passo de Newton quanto para um passo preditor-corretor, o próximo iterando pode ser expresso como uma função linear do parâmetro de penalização J1 e, no caso de um passo preditor-corretor, como uma função quadrática de J1. Mostramos também que essa parametrização é útil para garantir, por exemplo, a não-negatividade do próximo iterando ou sua proximidade da trajetória central. Resultados computacionais dessas estratégias são apresentados e comparados com PCx, uma implementação do método preditor-corretor de MehrotraAbstract: We study, in the context of interior-point methods for linear programming, some possible advantages of postponing the choice of the per. ",lty parameter and the step length, which happens both when we apply Newton's method to the Karush-Kuhn-Thcker system and when we apply a predictor-corrector scheme. We show that for a Newton or a strictly predictor step the next iterate can be expressed as a linear function of the penalty parameter J1, and, in the case of a predictor-corrector step, as a quadratic function of J1. We also show that this parameterization is useful to guarantee either the non-negativity of the next iterate or the proximity to the central path. Computational results of these strategies are shown and compared with PCx, an implementation of Mehrotra's predictor-corrector methodDoutoradoDoutor em Matemática Aplicad

Joint optimization of allocation and release policy decisions for surgical block time under uncertainty

The research presented in this dissertation contributes to the growing literature on applications of operations research methodology to healthcare problems through the development and analysis of mathematical models and simulation techniques to find practical solutions to fundamental problems facing nearly all hospitals. In practice, surgical block schedule allocation is usually determined regardless of the stochastic nature of case demand and duration. Once allocated, associated block time release policies, if utilized, are often simple rules that may be far from optimal. Although previous research has examined these decisions individually, our model considers them jointly. A multi-objective model that characterizes financial, temporal, and clinical measures is utilized within a simulation optimization framework. The model is also used to test “conventional wisdom” solutions and to identify improved practical approaches. Our result from scheduling multi-priority patients at the Stafford hospital highlights the importance of considering the joint optimization of block schedule and block release policy on quality of care and revenue, taking into account current resources and performance. The proposed model suggests a new approach for hospitals and OR managers to investigate the dynamic interaction of these decisions and to evaluate the impact of changes in the surgical schedule on operating room usage and patient waiting time, where patients have different sensitivities to waiting time. This study also investigated the performance of multiple scheduling policies under multi-priority patients. Experiments were conducted to assess their impacts on the waiting time of patients and hospital profit. Our results confirmed that our proposed threshold-based reserve policy has superior performance over common scheduling policies by preserving a specific amount of OR time for late-arriving, high priority demand

The tradeoff between growth and redistribution: ELIE in an overlapping generations model

education, growth, redistribution, Kolm

The Tradeoff Between Growth and Redistribution: ELIE in an Overlapping Generations Model

The ELIE scheme of Kolm taxes labour capacities instead of labour income in order to circumvent the distortionary effect of taxation on labour supply. Still, Kolm does not study the impact of ELIE on human capital formation and investment. In this paper, we build an overlapping generations (OLG) model with heterogenous agents and endogenous growth driven by investment in human capital. We study the effect of ELIE on education investment and other aggregate economic variables. Calibrating the model to French data, we highlight a tradeoff between growth and redistribution. With a perfect credit market, ELIE is successful in reducing inequalities and poverty, but it is at the expense of lower investment in education and slower growth. In an economy with an imperfect credit market where individuals cannot borrow to educate, the tradeoff between growth and redistribution is not overturned but is less severe. However, it is possible to overturn completely that trade-off simply by changing the base of taxation for the young generation which is equivalent to subsidising education.Education, Growth, Redistribution, Kolm

The non-permanence of optimal soil carbon sequestration

Carbon sequestration in agricultural soils is considered as an option of greenhouse gas mitigation in many countries. But, the economic potential is limited by the dynamic process of saturation and the opportunity cost of land use change. In addition, this article shows that permanence cannot, in general, be achieved in the strict sense of maintaining the soil carbon stock on an increased equilibrium level. Rather, a cyclical pattern with periodical release of sequestered carbon can be economically optimal from both the farmers’ and societal point of view.Agriculture, Climate policy, Carbon sequestration, Land use change, Economic analysis., Land Economics/Use, Q15, Q24, Q54.,

The Transactional Conflict Problem

The transactional conflict problem arises in transactional systems whenever two or more concurrent transactions clash on a data item. While the standard solution to such conflicts is to immediately abort one of the transactions, some practical systems consider the alternative of delaying conflict resolution for a short interval, which may allow one of the transactions to commit. The challenge in the transactional conflict problem is to choose the optimal length of this delay interval so as to minimize the overall running time penalty for the conflicting transactions. In this paper, we propose a family of optimal online algorithms for the transactional conflict problem. Specifically, we consider variants of this problem which arise in different implementations of transactional systems, namely "requestor wins" and "requestor aborts" implementations: in the former, the recipient of a coherence request is aborted, whereas in the latter, it is the requestor which has to abort. Both strategies are implemented by real systems. We show that the requestor aborts case can be reduced to a classic instance of the ski rental problem, while the requestor wins case leads to a new version of this classical problem, for which we derive optimal deterministic and randomized algorithms. Moreover, we prove that, under a simplified adversarial model, our algorithms are constant-competitive with the offline optimum in terms of throughput. We validate our algorithmic results empirically through a hardware simulation of hardware transactional memory (HTM), showing that our algorithms can lead to non-trivial performance improvements for classic concurrent data structures