Summary of the content
The purpose of this article was to use “a spatial microsimulation model to calculate the rates of poverty for small areas in Australia” (Tanton, Harding & McNamara 2010, p. 52).
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The spatial microsimulation approach used in this research involved “reweighting data from Confidentialised Unit Record Files (CURFs) from surveys conducted by the Australian Bureau of Statistics (ABS) to small area census data, also from the ABS” (Tanton et al. 2010, p. 52). The authors considered spatial microsimulation as a substitute for other methods used in spatial analysis (Tanton et al. 2010).
Microsimulation considers small area in spatial analysis. The maps of poverty rates were obtained from small area estimation. They covered the eastern coast of Australia and its capital cities. The researchers further conducted “poverty analysis to determine poverty rates in capital cities” (Tanton et al. 2010, p. 52).
From this research, the researchers established that specific areas of higher poverty risks could be effectively located within Sydney, Melbourne, Canberra and Brisbane (Tanton et al. 2010).
In addition, they also found that areas of high poverty were mainly “buffered’ by areas with moderate rates of poverty” (Tanton et al. 2010, p. 52). The researchers, however, noted that this was not often the case because there were some high poverty areas close to low poverty areas. Nevertheless, there were poverty buffers in most capital cities.
A critical analysis of the content
The article relied on previous studies to identify the gap in the field. For instance, the researchers noted that for a long time Australian researchers have experienced challenges related to producing neighbourhood level estimates of household features such small cases of poverty or housing stress, which affect social policies. This shows that the researchers were able to identify limitations of available literature and fill the gap.
The study was based on past studies. It showed that past studies have used national measures to understand variations in socioeconomic characteristics of households (Harding, Lloyd & Greenwell 2001; Pfeffermann 2002). In addition, recent studies used in the research showed that Australia and the UK have focused on developing studies based on spatial microsimulation (Ballas, Clarke, Dorling Rigby & Wheeler 2006).
The researchers acknowledged that previous studies were used to develop the methodological framework for their research. As a result, they were able to apply it on available data from Australian Census and other survey data to estimate poverty rates in cities. The article also acknowledged sources of data used in the spatial microsimulation methodology.
Literature used in this study is current and sufficiently covers the issue under study. Overall, the study has a thorough literature review, which provides background of the study and supports the model and findings.
Appropriateness of materials used
From the reference list, one can notice that the researchers used literature from various sources such as books, journal articles, research reports, national study reports and statistics among others. For instance, the researchers noted that national sample surveys were key sources of data.
Australia conducts a census after every five years to determine changes in gross income of households and people and understand factors that influence income variations.
In addition, national data provided “highly sophisticated information on relative advantage and disadvantages on disposable measures such as cash income, adjusted incomes for households and incomes for supporting each household” (Tanton et al. 2010, p. 52).
Books provided background information on spatial microsimulation and small area factors. Therefore, the researchers were able to develop a synthetic estimate of every small area under investigation. In short, they noted that small area synthetic or estimation originated from the field of statistics and therefore, researchers could use microsimulation methodology as an alternative to other statistical models.
Articles used in the study provided recent practices and areas of knowledge gap in small area spatial analysis. As a result, the researchers were able to develop an appropriate microsimulation model for the study. They also identified richer information about household characteristics than information contained in the census.
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Maps for the study were obtained from Spatial Microsimulation- SpatialMSM07a applied to STINMOD06B. In addition, there were also data from CURFs that were reweighted and Survey of Income and Housing – 2002/03 and 2003/04 (Tanton et al. 2010).
Organisation of the reading and development of ideas
Tanton et al. (2010) presented their idea clearly and logically supported with an abstract that provided overview of key issues covered.
The article presented an introduction that covered past challenges in spatial microsimulation analyses. This section also showed that researchers had experienced challenges in their attempts to obtain small area socioeconomic data. At the same time, it also covered literature review that presented background and theoretical underpinnings for the study.
The researchers then moved to a methodology section. This part covered various stages in developing regional poverty estimates or weights. The researchers also presented research benchmark variables in this section. There were also details on how the weights were applied in the study and calculating poverty rates.
The application section described how a spatial microsimulation model could be applied in various situations. For instance, Tanton et al. (2010) showed that the model could be used in the calculation of small area poverty rates and reviewing the regional effect of tax changes. The analysis of urban areas showed how “poverty rates were distributed in various parts of capital cities studied” (Tanton et al. 2010, p. 58).
Hence, they showed clearly how microsimulation could be applied in in-depth analysis of urban poverty rates. Application in analysis of scenario modelling showed how the model could be used to understand effects of changes in taxes and transfer policies on a given population or household.
Finally, the study also presented a clear conclusion. They showed that spatial microsimulation could be used to analyse poverty distribution in capital cities. The researchers concluded that, “poverty in urban areas tended to cluster in Sydney, Melbourne and Canberra, but did not do so to the same extent in Brisbane” (Tanton et al. 2010, p. 62).
Presentation of the argument
It is imperative to recognise that the authors attempted to improve on challenges that Australian researchers encountered when handling spatial microsimulation analyses. Hence, many studies in this research tend to support the ideas expressed in the article. Nevertheless, the researchers acknowledged that it was difficult to overcome challenges posed by variations in population sizes.
Specifically, such differences caused problems in data analysis. For instance, larger cities were likely to have populations with similar characteristics and therefore identifying poverty concentrations could be difficult in such areas relative to less populated areas. The researchers noted that such problems could not be overcome completely, but they provided population weighted quintiles of poverty rate to address them.
The authors showed that their findings differed slightly with findings based on STINMOD data. They claimed that such differences were expected because STINMOD data on poverty rates were usually lower relative to “aggregated synthetic estimates from the Survey of Income and Housing” (Tanton et al. 2010, p. 57).
In addition, they also showed that other confounding variables, such as the use of government benefits and headcount measure of poverty rates, could have created significant differences.
Overall, the article presented a balanced argument with opposing findings alongside possible explanations for such variations.
Use of language and statements on the significance of the findings
The use of the language in the study to demonstrate research findings is appropriate. The researchers clearly stated the outcomes of the study and its application in various areas. It is imperative to note that the researchers compared their study findings with other past results for credibility. They acknowledged the causes of slight variations in outcomes.
Tanton et al. (2010) validated their results thoroughly and determined poverty distribution across various cities under study. They noted similarities in outcomes between synthetic and census data. This implies that the researchers did not overstate the importance of their study outcomes or applications.
The study also showed similar results to those obtained in the 2006 Census data maps. It is imperative to recognise that this study aimed to provide a spatial microsimulation model that could be used to measure poverty in small populations. A lack of such measures has been major obstacles for researchers in Australia.
Conclusion with a synopsis of the main learning from the research
Tanton et al. (2010) provided a clear conclusion of their study alongside an abstract with the general overview of the study. It was therefore simple for other researchers to understand the purpose and outcomes of the study.
For instance, in the conclusion section, the researchers restated the purpose of the research as the application of a spatial microsimulation methodology to analyse and understand poverty distribution in a given urban area. Most importantly, the conclusion provided specific areas alongside poverty rate distribution, as well as differences between areas.
They also showed that the study results were validated for accuracy and comparison with previous studies. There were insignificant differences between synthetic data and census data. While the study showed that the outcomes conformed with the findings obtained from 2006 Census data, the researchers did not provide implications or weaknesses of their study in the conclusion section.
Ballas, D, Clarke, G, Dorling, D, Rigby, J, & Wheeler, B 2006, ‘Using geographical information systems and spatial microsimulation for the analysis of health inequalities’, Health Informatics Journal, vol. 12, pp. 65–79.
Harding, A, Lloyd, R & Greenwell, H 2001, Financial Disadvantage in Australia 1990 to 2000: the Persistence of Poverty in a Decade of Growth, The Smith Family, Sydney.
Pfeffermann, D 2002, ‘Small area estimation- new developments and directions’, International Statistical Review, vol. 70, pp. 125–143.
Tanton, R, Harding, A, & McNamara, J 2010, ‘Urban and Rural Estimates of Poverty: Recent Advances in Spatial Microsimulation in Australia’, Geographical Research, vol. 48, no. 1, pp. 52–64. doi: 10.1111/j.1745-5871.2009.00615.x