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This chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive conditional quantile model and propose a new construction of quantile impulse response functions (QIRFs). The tool set of QIRFs provides detailed distributional evolution of an outcome variable to economic shocks. The authors show the left tail of economic activity is the most responsive to monetary policy and financial shocks. The impacts of the shocks on Growth-at-Risk (the 5% quantile of economic activity) during the Global Financial Crisis are assessed. The authors also examine how the economy responds to a hypothetical financial distress scenario.

Revealed preference axioms provide a simple way of testing data from consumers or firms for consistency with optimizing behavior. The resulting non-parametric tests are very attractive, since they do not require any ad hoc functional form assumptions. A weakness of such tests, however, is that they are non-stochastic. In this paper, we provide a detailed analysis of two non-parametric approaches that can be used to derive statistical tests for utility maximization, which account for random measurement errors in the observed data. These same approaches can also be used to derive tests for separability of the utility function.

We use the Duclos, Makdissi and Wodon (2005) decomposition of programme dominance into targeting dominance and allocation dominance curves to identify poverty-reducing programme reforms. In particular we recognise the importance of considering more than one dimension when identifying poverty-reducing policy reforms. For this purpose, we use sequential stochastic dominance methods to analyse the poverty impact of policy programme reforms, particularly the case of income transfers and commodity taxation.

This chapter investigates the effect of financial performance and corporate governance on market performance, using evidence from the companies listed on the IDX30 Index of the Indonesia Stock Exchange (IDX) from 2015 to 2018. The authors use six main independent variables and one dependent variable, controlled by using control variables in the regression analysis. Ordinary least square (OLS) regression methods are used to model the relationship between the dependent variable and the independent variables. The results show that the current ratio (CR) and Board Size (BS) have a significant negative effect on stock return (SR). In contrast, the quick ratio (QR) and debt to equity ratio (DER) have a significant positive impact on SR. Both the debt to asset ratio (DAR) and Independent Board of Commissioners (BOC) have an insignificant effect on SR. This evidence suggests that the CR, QR, DER, and BS are essential factors affecting SR.

Numerous empirical studies have been conducted to analyze the impact of board gender diversity (BGD) on firm performance without being able to establish a clear relationship. In this paper, we reassess the relationship between BGD and firm performance by using a quantile regression approach. Our results indicate that BGD matters only across a subset of the firm performance distribution. Moreover, when the possible endogeneity of the relationship between BGD and firm performance is taken into account, there are some conditions under which a positive and significant relationship is observed for the eight lowest quantiles.

This chapter presents the perspective of an Information and Communications Technology (ICT) integration specialist on how mobile devices and apps are being used in several Western Australian primary schools to improve students’ literacy across a range of contexts and curriculum areas. In her role, the author is responsible for assisting teachers in Independent sector schools with Technologies, ICT Literacy and Science, Technology, Engineering and Mathematics (STEM) education and has worked extensively in helping teachers design rich cross-curricular tasks and programmes that harness a range of digital technologies, including mobile devices. The chapter presents several examples of how teachers in Western Australian Independent schools have used mobile tools across the curriculum in rich tasks, and how this has supported students’ literacy learning. Although this chapter makes specific references to Australian curricular requirements and contexts, it is envisaged that the practical examples and insights presented will be more broadly applicable in helping practitioners use mobile technologies to enhance literacy learning across the curriculum.

This study investigates the formal/informal employment earnings gap in Turkey. We focus on the earnings differentials that can be explained by observable characteristics and unobservable time-invariant individual heterogeneity. We first, estimate the standard Mincer earnings equations using ordinary least squares (OLS), controlling for individual, household, and job characteristics. Next we use, panel data and the quantile regression (QR) techniques in order to account for unobserved factors which might affect the earnings and the intrinsic heterogeneity within formal and informal sectors. OLS results confirm the existence of an informal sector penalty almost half of which is explained by observable variables. We find that formal-salaried workers are paid significantly higher than their informal counterparts and of the self-employed confirming the heterogeneity within the informal employment. QR results show that pay differentials are not uniform along the earnings distribution. In contrast to the mainstream literature which views informal self-employment as the upper-tier and wage-employment as the lower-tier, we find that self-employment corresponds to the lower-tier in the Turkish labor market. Finally, fixed effects estimation indicates that unobserved individual characteristics combined with controls for observable characteristics explain the pay differentials between formal and informal employment entirely in the total and the female sample. However, informal sector penalty persists in the male sample.

Data has become one of the most significant instruments in e-commerce innovation. Benefits to the entire society can be summarized as following: from the government’s perspective - to assess the impact of e-commerce to the economy; for merchants - to understand consumers’ needs; and for consumers - to be offered with the right product he/she is looking for. The digital revolution in the past five years has shown the need to offer more differentiated services than the physical stores, when consumers are not able to try and touch products. It is for this reason that e-commerce has continuously developed and transformed Research Online, Purchase Offline into a true experience. Considering the future of e-commerce is to enhance economic development and growth, this research will discuss the disruption of Research and Development through big data. The core objective of this research is to propose a predictive model to deeply understand consumer behavior by analyzing new regulations and transaction records.