Social Security, Demographic Trends, and Economic Growth: Theory and Evidence from the International Experience

The worldwide problem with pay-as-you-go (PAYG) social security systems isn't just financial. This study indicates that these systems may have exerted adverse effects on key demographic factors, private savings, and long-term growth rates. Through a comprehensive endogenous-growth model where human capital is the engine of growth, family choices affect human capital formation, and family formation itself is a choice variable, we show that social security taxes and benefits can create adverse incentive effects on family formation and subsequent household choices, and that these effects cannot be fully neutralized by counteracting intergenerational transfers within families. We implement the model using calibrated simulations as well as panel data from 57 countries over 32 years (1960-92). We find that PAYG tax measures account for a sizeable part of the downward trends in family formation and fertility worldwide, and for a slowdown in the rates of savings and economic growth, especially in OECD countries.

Minimum Density Hyperplanes

Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning approaches to the classification of unlabelled data. We propose a novel hyperplane classifier for clustering and semi-supervised classification which is motivated by this objective. The proposed minimum density hyperplane minimises the integral of the empirical probability density function along it, thereby avoiding intersection with high density clusters. We show that the minimum density and the maximum margin hyperplanes are asymptotically equivalent, thus linking this approach to maximum margin clustering and semi-supervised support vector classifiers. We propose a projection pursuit formulation of the associated optimisation problem which allows us to find minimum density hyperplanes efficiently in practice, and evaluate its performance on a range of benchmark datasets. The proposed approach is found to be very competitive with state of the art methods for clustering and semi-supervised classification

Generalized Sparse Discriminant Analysis for Event-Related Potential Classification

A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve this type of classification problems, but it fails when the number of features is large relative to the number of observations. In this work we propose a penalized version of the sparse discriminant analysis (SDA), called generalized sparse discriminant analysis (GSDA), for binary classification. This method inherits both the discriminative feature selection and classification properties of SDA and it also improves SDA performance through the addition of Kullback-Leibler class discrepancy information. The GSDA method is designed to automatically select the optimal regularization parameters. Numerical experiments with two real ERP-EEG datasets show that, on one hand, GSDA outperforms standard SDA in the sense of classification performance, sparsity and required computing time, and, on the other hand, it also yields better overall performances, compared to well-known ERP classification algorithms, for single-trial ERP classification when insufficient training samples are available. Hence, GSDA constitute a potential useful method for reducing the calibration times in ERP-based BCI systems.Fil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Spies, Ruben Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentin

Resource Devolution from the Centre to States: Enhancing the Revenue Capacity of States for Implementation of Essential Health Interventions

Access to adequate health care services is an important component of empowering people with human capital. This, however, can be achieved only when the spending on health care is adequate and delivery systems efficient. Improving health indicators is an important component of the Millennium Development Goals (MDGs) set by the United Nations. There are also important targets on health status achievements set for the Tenth Plan. The Common Minimum Programme of the ruling UPA government also seeks to increase the public expenditure by the Centre and States on health and family welfare schemes from the present level of less than 1% to 2%-3% of the gross domestic product (GDP). The provision of health and family welfare services falls in the realm of concurrent responsibility of the Centre and the States, but the latter have a predominant role in the delivery of these services. However, fiscal pressures at the State level lead to compression of expenditures by the State Governments resulting in an increase in Central financing of these services, particularly for some prioritized programmes implemented through the Centre and Centrally sponsored schemes. Thus, over 85% of the public expenditure on medical and public health is incurred by the State Governments, though the proportion of financing the expenditure by the State Governments is lower. This paper identifies the resource gap between the desired and the actual health expenditure in 15 major States in India (14 large, non-special category States and Assam), and highlights the extent to which the gap can be reduced by augmenting resources at the State level. Further, it estimates the resource gap that cannot be met through States’ own resources and therefore, requires Central transfers. The design of Central transfers needed for meeting the required health expenditure of various States is also discussed.Federal Transfers to Provinces; Public Expenditure on Health

The match between climate services demands and Earth System Models supplies

Earth System Models (ESM) are key ingredients of many of the climate services that are currently being developed and delivered. However, ESMs have more applications than the provision of climate services, and similarly many climate services use more sources of information than ESMs. This discussion paper elaborates on dilemmas that are evident at the interface between ESMs and climate services, in particular: (a) purposes of the models versus service development, (b) gap between the spatial and temporal scales of the models versus the scales needed in applications, and (c) Tailoring climate model results to real-world applications. A continued and broad-minded dialogue between the ESM developers and climate services providers’ communities is needed to improve both the optimal use and direction of ESM development and climate service development. We put forward considerations to improve this dialogue between the communities developing ESMs and climate services, in order to increase the mutual benefit that enhanced understanding of prospects and limitations of ESMs and climate services will bring.This work and its contributors (B. van den Hurk, C. Hewitt, J. Bessembinder, F. Doblas-Reyes, R. Döscher) were funded by the Horizon 2020 Framework Programme of the European Union: Project ref. 689029 (Climateurope project). The co-author and editor of the journal states that she was not involved in the review process of the paper.Peer ReviewedPostprint (published version