Reproduction of Social Capital: How Much and What Type of Social Capital Is Transmitted from Parents to Children?

The article analyzes the extent of the transmission of social capital from parents to their children. Three measures of social capital are used: social trust, participation in social activities and useful social connections. The data from the longitudinal extension of the PISA collected in the Czech Republic in 2003 are used. First, bivariate correlations of three types of social capital are analyzed. Second, using logistic regression, four theoretical models (the social capital model, the family background model, the personality model and the contextual model) are tested. As dependent variables we use the social trust of fifteen-year-olds and their participation in four types of extra-curricular activities. The analysis reveals only a weak intergenerational transmission of the same social capital types (“intergenerational line-up”) and almost no intergenerational transmission of different social capital types (“intergenerational cross-over”). No theoretical model is particularly strong in explaining the social trust of children. The social trust of youths remains largely unexplained and is created irrespectively of family cultural and financial capital. Conversely, participation in extra-curricular activities is highly socially stratified. It is substantially better predicted by all theoretical models, though their effect is dependent upon the activity at stake. The author concludes that social capital is comprised of several different forms of capital, which are only distantly related. The finding that family background has a relatively weak impact on children’s social trust but a strong effect on their participation of extra-curricular activities has profound implications for public policy.Social capital; social trust; political socialization; generations; the Czech Republic; youths

Tight Lower Bound for Comparison-Based Quantile Summaries

Quantiles, such as the median or percentiles, provide concise and useful information about the distribution of a collection of items, drawn from a totally ordered universe. We study data structures, called quantile summaries, which keep track of all quantiles, up to an error of at most $\varepsilon$. That is, an $\varepsilon$-approximate quantile summary first processes a stream of items and then, given any quantile query $0\le \phi\le 1$, returns an item from the stream, which is a $\phi'$-quantile for some $\phi' = \phi \pm \varepsilon$. We focus on comparison-based quantile summaries that can only compare two items and are otherwise completely oblivious of the universe. The best such deterministic quantile summary to date, due to Greenwald and Khanna (SIGMOD '01), stores at most $O(\frac{1}{\varepsilon}\cdot \log \varepsilon N)$ items, where $N$ is the number of items in the stream. We prove that this space bound is optimal by showing a matching lower bound. Our result thus rules out the possibility of constructing a deterministic comparison-based quantile summary in space $f(\varepsilon)\cdot o(\log N)$, for any function $f$ that does not depend on $N$. As a corollary, we improve the lower bound for biased quantiles, which provide a stronger, relative-error guarantee of $(1\pm \varepsilon)\cdot \phi$, and for other related computational tasks.Comment: 20 pages, 2 figures, major revison of the construction (Sec. 3) and some other parts of the pape

An iterative Newton\u27s method for output-feedback LQR design for large-scale systems with guaranteed convergence

The paper proposes a novel iterative output-feedback control design procedure, with necessary and sufficient stability conditions, for linear time-invariant systems within the linear quadratic regulator (LQR) framework. The proposed iterative method has a guaranteed convergence from an initial Lyapunov matrix, obtained for any stabilizing state-feedback gain, to a stabilizing output-feedback solution. Another contribution of the proposed method is that it is computationally much more tractable then algorithms in the literature, since it solves only a Lyapunov equation at each iteration step. Therefore, the proposed algorithm succeed in high dimensional problems where other, state-of-the-art methods fails. Finally, numerical examples illustrate the effectiveness of the proposed method

A ϕ\phi-Competitive Algorithm for Scheduling Packets with Deadlines

In the online packet scheduling problem with deadlines (PacketScheduling, for short), the goal is to schedule transmissions of packets that arrive over time in a network switch and need to be sent across a link. Each packet has a deadline, representing its urgency, and a non-negative weight, that represents its priority. Only one packet can be transmitted in any time slot, so, if the system is overloaded, some packets will inevitably miss their deadlines and be dropped. In this scenario, the natural objective is to compute a transmission schedule that maximizes the total weight of packets which are successfully transmitted. The problem is inherently online, with the scheduling decisions made without the knowledge of future packet arrivals. The central problem concerning PacketScheduling, that has been a subject of intensive study since 2001, is to determine the optimal competitive ratio of online algorithms, namely the worst-case ratio between the optimum total weight of a schedule (computed by an offline algorithm) and the weight of a schedule computed by a (deterministic) online algorithm. We solve this open problem by presenting a $\phi$-competitive online algorithm for PacketScheduling (where $\phi\approx 1.618$ is the golden ratio), matching the previously established lower bound.Comment: Major revision of the analysis and some other parts of the paper. Another revision will follo