Not without them: realising the sustainable development goals for women migrant workers

ABSTRACT

Drawing on multiple data sources, including key informant interviews, participant observation and archival study, this paper provides an analysis of the civil society’s role in foregrounding the agenda of women migrants in migration and development (M&D) fora, and reflects on its role in realising the UN Sustainable Development Goals (SDGs). Yet, the dominant narrative within the state-led Global Forum on Migration and Development (GFMD) tends to be a gender-blind migration for development approach, which emphasises national-level economic growth at the centre of migration processes, while negating the subjectivities of women migrants and neglecting their contributions to the global economy; this approach diverts attention to a narrow focus on macro-economic development through forms of financial remittances. Based on an examination of the GFMD as a site for gender mainstreaming M&D, we reflect on lessons learned as we look forward to achieving the SDGs. We argue that while the SDGs include some significant provisions for women in migration, only critical civil society advocacy and activism networked within grassroots organisations can address the structural changes necessary (such as a re-articulation of the care economy to value economic contributions of women’s reproductive work) to transform and improve the lived realities of women in migration and realise the SDGs in a manner that fosters their empowerment.     

Exact moments of the symmetric cubic assignment statistic

Hubert (1987Assignment Methods in Combinatorial Data Analysis) presented a class of permutation, or random assignment, techniques for assessing correspondence between general k-dimensional proximity measures on a set of “objects.” A major problem in higher-order assignment models is the prohibitive level of computation that is required. We present the first three exact moments of a test statistic for the symmetric cubic assignment model. Efficient computational formulas for the first three moments have been derived, thereby permitting approximation of the permutation distribution using well-known methods.     

Uncertainty Analysis Based on Probability Bounds (P-Box) Approach in Probabilistic Safety Assessment

A wide range of uncertainties will be introduced inevitably during the process of performing a safety assessment of engineering systems. The impact of all these uncertainties must be addressed if the analysis is to serve as a tool in the decision-making process. Uncertainties present in the components (input parameters of model or basic events) of model output are propagated to quantify its impact in the final results. There are several methods available in the literature, namely, method of moments, discrete probability analysis, Monte Carlo simulation, fuzzy arithmetic, and Dempster-Shafer theory. All the methods are different in terms of characterizing at the component level and also in propagating to the system level. All these methods have different desirable and undesirable features, making them more or less useful in different situations. In the probabilistic framework, which is most widely used, probability distribution is used to characterize uncertainty. However, in situations in which one cannot specify (1) parameter values for input distributions, (2) precise probability distributions (shape), and (3) dependencies between input parameters, these methods have limitations and are found to be not effective. In order to address some of these limitations, the article presents uncertainty analysis in the context of level-1 probabilistic safety assessment (PSA) based on a probability bounds (PB) approach. PB analysis combines probability theory and interval arithmetic to produce probability boxes (p-boxes), structures that allow the comprehensive propagation through calculation in a rigorous way. A practical case study is also carried out with the developed code based on the PB approach and compared with the two-phase Monte Carlo simulation results.     

Sampling Strategies for Toxic Air Contaminants

Annual concentrations of toxic air contaminants are of primary concern from the perspective of chronic human exposure assessment and risk analysis. Despite recent advances in air quality monitoring technology, resource and technical constraints often impose limitations on the availability of a sufficient number of ambient concentration measurements for performing environmental risk analysis. Therefore, sample size limitations, representativeness of data, and uncertainties in the estimated annual mean concentration must be examined before performing quantitative risk analysis. In this paper, we discuss several factors that need to be considered in designing field-sampling programs for toxic air contaminants and in verifying compliance with environmental regulations. Specifically, we examine the behavior of SO2, TSP, and CO data as surrogates for toxic air contaminants and as examples of point source, area source, and line source-dominated pollutants, respectively, from the standpoint of sampling design. We demonstrate the use of bootstrap resampling method and normal theory in estimating the annual mean concentration and its 95% confidence bounds from limited sampling data, and illustrate the application of operating characteristic (OC) curves to determine optimum sample size and other sampling strategies. We also outline a statistical procedure, based on a one-sided t-test, that utilizes the sampled concentration data for evaluating whether a sampling site is compliance with relevant ambient guideline concentrations for toxic air contaminants.     

Chinese grand(parenting) encourages a re-imagining of Canada's immigration regime

Canada's migration regime prioritizes the admission of young skilled immigrants while restricting elderly immigrants constructed as non-contributing dependents. We review and compare the current pathways to family reunification for elderly immigrants. Based on interviews with 16 Chinese skilled immigrant mothers and eight sponsored (grand)parents in Edmonton and Ottawa, we highlight the contributions made by elderly Chinese immigrants to social reproduction, intergenerational cultural preservation, and various forms of transnational support. We also reveal the financial and emotional costs incurred by young skilled immigrants who sought to sponsor their elderly parents for permanent residency. Based on these rich conversations, we propose two policy recommendations to reduce the pressure on sponsoring family members and create opportunities for elderly immigrants to access gainful employment.     

Search for optimal designs in a three stagenested random model

In this paper we define a class of unbalanced designs, denoted by C_k,s,t, for estimating the components of variance in a k-stage nested random effects linear model. This class contains many of the designs proposed in the literature for nested components of variance models. We focus on the three-state model and discuss the determination of locally optimal designs within this class using a systematic computer search. For large sample sizes we show that approximate optimal designs may be obtained using a limit argument combined with numerical optimization. A comparison of our designs with previously published designs suggests that, in many cases, our designs result in substantial gains in efficiency.     

Work, employment, and mental illness: expanding the domain of Canadian social work

Despite established evidence that work and employment are an important component of recovery for people who experience mental illness, social work education in Canada seldom offers graduate training or courses on the significance of work in peoples' lives or on the practices involved in helping to gain and retain employment for these individuals. In this article the authors argue that the high levels of unemployment among people who experience mental illness, and the rising incidence of mental health and addictions issues in workplaces, offer the opportunity, as well as the mandate, for social work educators to provide professional education in the area of employment support and assistance.     

Differences in Retail Inventory Investment Behavior During Macroeconomic Shocks: Role of Service Level

In this article, we analyze how retailers change their inventory investment behavior in response to macroeconomic shocks. We examine if service level, as measured by the ratio of stockout to inventory holding costs, can explain the differences in observed behavior across retailers. We use data on macroeconomic indicators and quarterly filings of US public retailers from 1985 to 2009 to estimate a dynamic model of short‐ and long‐term impact of macroeconomic shocks on inventory investment. Our results show that retailers with a high service level increase their inventory investment significantly more than those with a low service level during expansion shocks. Conversely, retailers with a low service level curtail their inventory investment significantly more than those with a high service level during periods of economic contractions. Thus, we show that the aggregate change in inventory investment documented in prior macroeconomics research is driven by different sets of retailers, as predicted by newsvendor logic. We draw implications of our findings to retailers as well as their suppliers.     

Fiducial prediction intervals

This paper presents an approach for constructing prediction intervals for any given distribution. The approach is based on the principle of fiducial inference. We use several examples, including the normal, binomial, exponential, gamma, and Weibull distributions, to illustrate the proposed procedure.