School Fees, Parental Participation and Accountability: Evidence from Madagascar

The role of school fees in achieving both allocative and productive efficiency in the delivery of primary education has been a subject of intense debate. Building on a simple model that makes explicit the role of school fees in determining the optimal level of parental participation to school governance, this paper contributes to the debate by evaluating empirically the relationship between fees, participation and the accountability framework in public primary schools in Madagascar. The results show evidence that schools requiring parents to pay more fees experience a higher degree of parental participation. While results are consistent with the theoretical model, the empirical analysis provides evidence that school fees increase participation beyond their effect on the power relationship between the community and the school authorities. The model hypothesis that school fees modify the accountability framework, which leads to more productive participation efforts, is challenged by alternative explanations. One of them is that participation aims not to increase education quality but rather to decrease the amount of fees requested by the school

Feature context-dependency and complexity-reduction in probability landscapes for integrative genomics

<p>Abstract</p> <p>Background</p> <p>The question of how to integrate heterogeneous sources of biological information into a coherent framework that allows the gene regulatory code in eukaryotes to be systematically investigated is one of the major challenges faced by systems biology. Probability landscapes, which include as reference set the probabilistic representation of the genomic sequence, have been proposed as a possible approach to the systematic discovery and analysis of correlations amongst initially heterogeneous and un-relatable descriptions and genome-wide measurements. Much of the available experimental sequence and genome activity information is <it>de facto</it>, but not necessarily obviously, context dependent. Furthermore, the context dependency of the relevant information is itself dependent on the biological question addressed. It is hence necessary to develop a systematic way of discovering the context-dependency of functional genomics information in a flexible, question-dependent manner.</p> <p>Results</p> <p>We demonstrate here how feature context-dependency can be systematically investigated using probability landscapes. Furthermore, we show how different feature probability profiles can be conditionally collapsed to reduce the computational and formal, mathematical complexity of probability landscapes. Interestingly, the possibility of complexity reduction can be linked directly to the analysis of context-dependency.</p> <p>Conclusion</p> <p>These two advances in our understanding of the properties of probability landscapes not only simplify subsequent cross-correlation analysis in hypothesis-driven model building and testing, but also provide additional insights into the biological gene regulatory problems studied. Furthermore, insights into the nature of individual features and a classification of features according to their minimal context-dependency are achieved. The formal structure proposed contributes to a concrete and tangible basis for attempting to formulate novel mathematical structures for describing gene regulation in eukaryotes on a genome-wide scale.</p

Interplay between Topology and Dynamics in Excitation Patterns on Hierarchical Graphs

In a recent publication (Müller-Linow et al., 2008) two types of correlations between network topology and dynamics have been observed: waves propagating from central nodes and module-based synchronization. Remarkably, the dynamic behavior of hierarchical modular networks can switch from one of these modes to the other as the level of spontaneous network activation changes. Here we attempt to capture the origin of this switching behavior in a mean-field model as well in a formalism, where excitation waves are regarded as avalanches on the graph