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Parental job loss has been shown to have a negative impact on a large number of children's outcomes, in particular for low-income children. Given the amount of time freed up by loss of employment and the fact that active time with one's children appears to be a productive input in their human capital production function, increases in the time parents spend with their children have the potential to positively impact a child or to counteract other negative consequences of parental job loss. This chapter studies how low- and higher-income parents change their time investment in their children when faced with job loss. Using national time-diary data from the American Time Use Survey (ATUS) linked to longitudinal labor market data from the Current Population Survey (CPS), I find that parents spend almost 15% of the time freed up by job loss – roughly 3.5 additional hours per week – actively with their children. Low-income parents invest their freed-up time no differently from higher-income parents. While mothers who lose their job respond by spending more time actively with their children, this adjustment is much smaller for fathers. This suggests that differential adjustments in time investment may play a role in the impact maternal versus paternal job loss has on children's outcomes.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

It is now well-established that good water quality is associated with economic prosperity, reduced incidence on public health and the good functioning of the various ecosystems found in our environment. Water contamination is mostly related to both diffused (agricultural lands and geologic rock degradations) and point sources of pollution. Mauritius has many water resources which depend solely on precipitation for their replenishment. Water parameters which are of relevance include total dissolved solids (TDS), temperature, pH, electrical conductivity, turbidity, dissolved oxygen, dissolved and particulate organic carbon and major cations and anions. The traditional methods of analysis for these parameters are mostly using electrical and optical methods (probes and sensors in the field), while chemical titrations, Flame AAS and High-Performance Liquid Chromatography techniques are carried out in the laboratory. Image Classification techniques using neural networks can also be used to detect the presence of contaminants in water. In addition to basic water quality parameters, the field sensors range have been extended to cover important major ions and can now be integrated with Artificial Intelligence (AI)-based models for the prediction of variations in water quality to better protect human health and the environment, reduce operation costs of water and wastewater treatment plant unit processes.