Literacy and numeracy skills and labour market outcomes in Australia

Australian adults are above the OECD average in literacy but only average in numeracy, according to a staff paper released by the Productivity Commission. The paper analyses the profile of adult literacy and numeracy skills in Australia, and how important those skills are for labour market outcomes. Key points: Adult literacy and numeracy skills contribute to wellbeing in many ways. At an individual level, they are central to social and economic participation. Literacy and numeracy skills are a core part of a person\u27s human capital. They also support the development of other forms of human capital, including knowledge, other skills and health. Some Australians have low (level 1 or below) literacy and numeracy skills. In 2011–12: 14 per cent of Australians could, at best, read only relatively short texts from which they were able to locate only a single piece of information. 22 per cent could only carry out one-step or simple processes such as counting where the mathematical content is explicit with little or no text or distractors. At the other end of the skill distribution, 16 per cent of Australians had high (level 4/5) literacy skills and 12 per cent had high numeracy skills in 2011–12. People with high literacy skills can make complex inferences and evaluate subtle truth claims or arguments in lengthy or multiple texts. People with high numeracy skills can understand a broad range of mathematical information that may be complex, abstract or embedded in unfamiliar contexts. Most Australians have skills somewhere between these levels. Groups with relatively low literacy and numeracy skills include: people with low levels of education; older persons; people not working; and immigrants with a non-English speaking background. Compared with other countries in the OECD, Australia performs above average on literacy but average in numeracy. Higher literacy and numeracy skills are associated with better labour market outcomes (employment and wages). Econometric modelling shows that: an increase in literacy and numeracy by one skill level is associated with an increased likelihood of employment of 2.4 and 4.3 percentage points for men and women, respectively an increase in literacy and numeracy skills is associated with a similar increase in the probability of employment, whether a person had a degree, diploma/certificate or Year 12 education an increase in literacy and numeracy by one skill level is associated with about a 10 per cent increase in wages for both men and women. This positive association is equivalent to that of increasing educational attainment from Year 11 to Year 12 or to a diploma/certificate up to 40 per cent of the association between education and employment is attributable to literacy and numeracy skills. These results are consistent with education providing many other attributes of human capital that are valued in the workplace more than half of the \u27penalty\u27 that affects the wages of people with a non-English speaking background is explained by their lower literacy and numeracy skills. • Staff working papers are not formal publications of the Commission. They have been prepared and are authored by individual staff to advance understanding of issues on the Commission’s supporting research program

Investigating synthesis of Cu2ZnSn(Se1-x,Sx)4 for values of 0≤x≤1 by S for Se substitution and direct sulphidisation of metallic precursors

Thin layers of Cu2ZnSn(Se1-x,Sx)4 were produced by selenisation and subsequent sulphur substitution of DC sputter-deposited metallic CZT precursors on soda-lime glass. Values of x=(0, 0.07, 0.12, 0.17, 0.28, 1) were measured by EDS. Samples were characterised optically and analysed using the Kubelka-Munk function, and found to have 0.96eV ≤Eg≤ 1.47eV, varying approximately linearly with x. Samples underwent X-ray diffraction characterisation and substituted samples were found to comprise of multiple phase kesterite material with different levels of S substitution, averaging to the values obtained by EDS. The spectra were found to conform to Vegard's law, as peak location shifted linearly between x=0 and x=1. Binary phases are suspected to exist, because of some unusual behaviour at the location of the (200) peak. Lattice parameters for all phases were calculated and found to vary linearly between (a=b=5.692, c=11.338) for x=0 and (a=b= 5.393, c= 10.863) for x=1, which are in excellent agreement with previously published figures

Great Bay Nitrogen Non-Point Source Study

The Great Bay Estuary is 21 square miles of tidal waters located in southeastern New Hampshire. It is one of 28 “estuaries of national significance” established under the Environmental Protection Agency’s National Estuary Program. The estuary is experiencing the signs of eutrophication, specifically, low dissolved oxygen, macroalgae blooms, and declining eelgrass habitat (DES, 2012). Sixty-eight percent of the nitrogen that ends up in the Great Bay Estuary originates from sources spread across the watershed; the remainder derives from direct discharges of municipal wastewater treatment facilities (DES, 2010; PREP, 2013). In this report, these sources of nitrogen are called non-point sources and consist of atmospheric deposition, fertilizers, human waste disposed into septic systems, and animal waste. The purpose of this study is to determine how much nitrogen each non-point source type contributes to the estuary. The nitrogen loads from municipal wastewater treatment facilities have been reported elsewhere (DES, 2010; PREP, 2012; PREP, 2013) and, therefore, are not included in this study except to provide context. The intended use of this study is for planning purposes, and is not meant for regulatory allocations or specific reduction requirements. The results of the model may be useful for towns or watershed groups for prioritizing nitrogen reduction efforts or as a starting point for more detailed studies of non-point sources. However, more detailed inventories of non-point sources will be needed to track the effects of nitrogen reduction efforts in smaller areas. In addition, the model makes no conclusions about the benefits of nitrogen reductions to receiving waters or overall estuarine health

Centering Work: Integration and Diffusion of Workforce Development within the U.S. Manufacturing Extension Network

As the U.S. economy rebounds from the COVID-19 pandemic, strategies that promote long-term transformation toward high-quality jobs will be critical. This includes workplace-improving interventions that enable employers to upgrade existing jobs, often while enhancing their own competitive position. This paper focuses on the Manufacturing Extension Partnership, a national network of federally funded centers that support small and medium-sized manufacturing firms. We document the range of workforce- and workplace-enhancing strategies that MEP centers have adopted since the network’s inception in the mid-1990s. While workforce development is unevenly implemented across today’s MEP network, leading centers within the network are devising transformative strategies that shape underlying business practices in ways that can improve the quality of front-line manufacturing jobs. The pandemic recovery, along with federal commitment to reenergize domestic supply chains, presents an opportunity to establish NIST-MEP as a national workforce-development leader while also strengthening localized institutional partnerships to center that effort on inclusive economic development and recovery

Identity Testing and Lower Bounds for Read-k Oblivious Algebraic Branching Programs

Read-k oblivious algebraic branching programs are a natural generalization of the well-studied model of read-once oblivious algebraic branching program (ROABPs). In this work, we give an exponential lower bound of exp(n/k^{O(k)}) on the width of any read-k oblivious ABP computing some explicit multilinear polynomial f that is computed by a polynomial size depth-3 circuit. We also study the polynomial identity testing (PIT) problem for this model and obtain a white-box subexponential-time PIT algorithm. The algorithm runs in time 2^{~O(n^{1-1/2^{k-1}})} and needs white box access only to know the order in which the variables appear in the ABP