Annual Report on the Statistics of Labor

Issued by the Bureau of Statistics of Labor, 1870-1907; by the Bureau of Statistics, 1908-1919; by the Dept. of Labor and Industries. Division of Statistics, 1920-1958; by the Dept. of Labor and Industries, 1959-1962

Guide books of excursions in Canada. 1-10

Mode of access: Internet.

Biennial Report and Industrial Directory of the Bureau of Labor and State Wage and Hour Commission of the State of Oregon

Title Varies: 1st-13th Biennial Report of the Bureau of Labor Statistics and Inspector of Factories and Workshops. 1903/04-1926/38 (1918/20-1926/28, Biennial Report and Industrial Directory); 14th Biennial Report and Industrial Directory of the Bureau of Labor

Preliminary abstract of railway statistics (steam railways, Railway Express Agency, Inc., and the Pullman Company) ...

At head of title, 1910/11-1911/12: Interstate Commerce Commission; 1912/13-1915/16: Interstate Commerce Commission. Division of Statistics; 1916-53: Interstate Commerce Commission. Bureau of Statistics.

Report on Productive Industries, Public Utilities and Miscellaneous Statistics of the Commonwealth of Pennsylvania

Mode of access: Internet.

Report by the Government Statistician on the vital statistics of New South Wales.

Other slight variations in title.

Audio-visual products from the Bureau of the Census.

Mode of access: Internet.

1992 census of Agriculture data for states and counties : news on the latest data products from the U.S. Bureau of the Census.

Mode of access: Internet.

Exploratory spatial analysis of social and environmental factors associated with the incidence of Ross River Virus in Brisbane, Australia

We used geographic information systems and a spatial analysis approach to explore the pattern of Ross River virus (RRV) incidence in Brisbane, Australia. Climate, vegetation and socioeconomic data in 2001 were obtained from the Australian Bureau of Meteorology, the Brisbane City Council and the Australian Bureau of Statistics, respectively. Information on the RRV cases was obtained from the Queensland Department of Health. Spatial and multiple negative binomial regression models were used to identify the socioeconomic and environmental determinants of RRV transmission. The results show that RRV activity was primarily concentrated in the northeastern, northwestern, and southeastern regions in Brisbane. Multiple negative binomial regression models showed that the spatial pattern of RRV disease in Brisbane seemed to be determined by a combination of local ecologic, socioeconomic, and environmental factors.

Competitiveness of the Indonesian construction industry

Indonesia’s construction industry is important to the national economy. However, its competitiveness is considered low due to the lack of success of its development strategy and policy. A new approach known as the cluster approach is being used to make strategy and policy in order to develop a stronger, and more competitive industry. This paper discusses the layout of the Indonesian construction cluster and its competitiveness. The archival analysis research approach was used to identify the construction cluster. The analysis was based on the input-output (I/O) tables of the years 1995 and 2000, which were published by the Indonesian Central Bureau of Statistics. The results suggest that the Indonesian construction cluster consists of the industries directly involved in construction as the core, with the other related and supporting industries as the balance. The anatomy of the Indonesian construction cluster permits structural changes to happen within it. These changes depend on policies that regulate the cluster’s constituents

Safer construction voluntary code of practice : implications for SMEs

The majority of Australian construction firms are small businesses, with 97% of general construction businesses employing less than 20 employees and 85% employing less than five employees (Lin and Mills, 2001; Lingard and Holmes, 2001). The Australian Bureau of Statistics’ definition of a small to medium enterprise was used for the purpose of this study (McLennan, 2000). This included small business employing less than twenty people and medium business employing less than 200 people. Although small to medium enterprises (SME) make up the major share of construction organisations in Australia, there is a paucity of published research in relation to occupational health and safety (OHS) issues for this group. Typically, SME organisations “are frequently undercapitalized and depend on continuous cash flow for their continued business” (Cole, 2003; 12). Research by Lin and Mills (2001) indicates that these factors influence the smaller operators’ ability and motivation to achieve high levels of OHS compared to larger firms which tend to integrate OHS into their management systems. According to Lin and Mills (2001; 137) small firms “do not feel the need to focus on OHS in their management systems, instead they often believe that the control of risk is the responsibility of employees”. This report documents findings from a qualitative research study that examined SME organisations’ views of a newly developed voluntary code of practice (VCOP), and ways in which they might implement the code in their businesses. The research also explored respondents’ awareness of current safety issues in industry in the context of their personal experiences.

The effectiveness of using a simple ARIA based geographical classification to identify road crash patterns in rural and urban areas of Queensland

Research has noted a ‘pronounced pattern of increase with increasing remoteness' of death rates in road crashes. However, crash characteristics by remoteness are not commonly or consistently reported, with definitions of rural and urban often relying on proxy representations such as prevailing speed limit. The current paper seeks to evaluate the efficacy of the Accessibility / Remoteness Index of Australia (ARIA+) to identifying trends in road crashes. ARIA+ does not rely on road-specific measures and uses distances to populated centres to attribute a score to an area, which can in turn be grouped into 5 classifications of increasing remoteness. The current paper uses applications of these classifications at the broad level of Australian Bureau of Statistics' Statistical Local Areas, thus avoiding precise crash locating or dedicated mapping software. Analyses used Queensland road crash database details for all 31,346 crashes resulting in a fatality or hospitalisation occurring between 1st July, 2001 and 30th June 2006 inclusive. Results showed that this simplified application of ARIA+ aligned with previous definitions such as speed limit, while also providing further delineation. Differences in crash contributing factors were noted with increasing remoteness such as a greater representation of alcohol and ‘excessive speed for circumstances.' Other factors such as the predominance of younger drivers in crashes differed little by remoteness classification. The results are discussed in terms of the utility of remoteness as a graduated rather than binary (rural/urban) construct and the potential for combining ARIA crash data with census and hospital datasets.

Preliminary spatiotemporal analysis of the association between socio-environmental factors and suicide

Background: The seasonality of suicide has long been recognised. However, little is known about the relative importance of socio-environmental factors in the occurrence of suicide in different geographical areas. This study examined the association of climate, socioeconomic and demographic factors with suicide in Queensland, Australia, using a spatiotemporal approach. Methods: Seasonal data on suicide, demographic variables and socioeconomic indexes for areas in each Local Government Area (LGA) between 1999 and 2003 were acquired from the Australian Bureau of Statistics. Climate data were supplied by the Australian Bureau of Meteorology. A multivariable generalized estimating equation model was used to examine the impact of socio-environmental factors on suicide. Results: The preliminary data analyses show that far north Queensland had the highest suicide incidence (e.g., Cook and Mornington Shires), while the south-western areas had the lowest incidence (e.g., Barcoo and Bauhinia Shires) in all the seasons. Maximum temperature, unemployment rate, the proportion of Indigenous population and the proportion of population with low individual income were statistically significantly and positively associated with suicide. There were weaker but not significant associations for other variables. Conclusions: Maximum temperature, the proportion of Indigenous population and unemployment rate appeared to be major determinants of suicide at a LGA level in Queensland.

Spatial analysis of notified cryptosporidiosis infections in Brisbane, Australia

Purpose: This study explored the spatial distribution of notified cryptosporidiosis cases and identified major socioeconomic factors associated with the transmission of cryptosporidiosis in Brisbane, Australia. Methods: We obtained the computerized data sets on the notified cryptosporidiosis cases and their key socioeconomic factors by statistical local area (SLA) in Brisbane for the period of 1996 to 2004 from the Queensland Department of Health and Australian Bureau of Statistics, respectively. We used spatial empirical Bayes rates smoothing to estimate the spatial distribution of cryptosporidiosis cases. A spatial classification and regression tree (CART) model was developed to explore the relationship between socioeconomic factors and the incidence rates of cryptosporidiosis. Results: Spatial empirical Bayes analysis reveals that the cryptosporidiosis infections were primarily concentrated in the northwest and southeast of Brisbane. A spatial CART model shows that the relative risk for cryptosporidiosis transmission was 2.4 when the value of the social economic index for areas (SEIFA) was over 1028 and the proportion of residents with low educational attainment in an SLA exceeded 8.8%. Conclusions: There was remarkable variation in spatial distribution of cryptosporidiosis infections in Brisbane. Spatial pattern of cryptosporidiosis seems to be associated with SEIFA and the proportion of residents with low education attainment.

International comparisons of Malaysian construction labour productivity

Productivity is basic statistical information for many international comparisons and country performance assessments. This study estimates the construction labour productivity of 79 selected economies. The real (purchasing power parities converted) and nominal construction expenditure from the Report of 2005 International Comparison Programme published by the World Bank and construction employment from the database of labour statistics (LABORSTA) operated by the Bureau of Statistics of International Labour Organization were used in the estimation. The inference statistics indicate that the descending order of nominal construction labour productivity from high income economies to low income economies is not established. The average construction labour productivity of low income economies is higher than middle income economies when the productivity calculation uses purchasing power parities converted data. Malaysia ranked 50th and 63rd position among the 79 selected economies on real and nominal measurement respectively.