Economic Prosperity Index

Introduction

The Economic Prosperity Index (EPI) is an indicator that identifies those SA2s that have higher proportions of workers with characteristics that are thought to bring economic prosperity. The index is calculated on seven variables which, after a principal components analysis (PCA) are weighted to give an EPI score. All EPI scores across the country are compared relative to each other and placed in groups, where the SA2s with higher EPI scores are considered more prosperous than those with lower scores. The EPI is calculated for SA2s that make up the 101 Significant Urban Areas (SUAs) across Australia, that have a working population of more than 50.

More information on the Employment Prosperity Index can be found in this document

Inputs

We will run the Employment Prosperity Index across the whole of Australia, to see if we can replicate the “pre-packaged” EVI produced for use already in the AURIN portal

To do this

  • Select Australia as your area
  • Select Employment Prosperity Index for Australia 2011 Data Profile as your dataset, selecting all variables

Once you have selected selected your area and data, open the Economic Prosperity Index tool and enter the parameters as shown below. These are explained underneath the image.

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  • Dataset Input: Choose the dataset that you would like to include in the EVI calculation. In this portal, the dataset which feeds into this has been generated, and we use it here – Employment Vulnerability Index for Australia 2011 Data Profile
  • Key Column: The column specifying the individual codes for the areas that are included in the analysis. In this instance we specify SA2 Code 2011
  • Employment Variable: This parameter covers the employment category. There are a number that we could included, but in this instance we select Employment Rate
  • Full-time Employment Variable: This parameter deals with rates of full time employment. In this instance we select Full-time Employed as  Proportion of total Employed
  • Education Variable: This parameter covers the education component of the analysis. There is only one variable to include here: Proportion of Working Age Persons without a Post-School Qualification
  • High Wage Industry Variable: here we specify which high wage industry column will be included in the analysis. There are a number of ones that we can select, but in this instance we select Proportion Employed in High Wage Industries
  • Growth Industry Variable: here we specify which high growth industry column will be included in the analysis. There are a number of ones that we can select, but in this instance we select Proportion Employed in Growth Industries
  • Income Variable: there are a number of variables that could be included here, but we will select Median Individual Income
  • Government Benefits Variable: There is only one variable that can be included here: Average Government Benefits
  • Employment Weight: This specifies the weighting of the employment variable in the analysis – we use the default value of 0.37
  • Full-time Employment Weight: This specifies the relative weighting of the full time employment variable in the analysis – we use the default value of 0.27
  • Education Weight: This specifies the relative weighting of the education variable in the analysis – we use the default value of 0.40
  • High Wage Industry Weight: This specifies the relative weighting of the high wage industry variable in the analysis – we use the default value of 0.43
  • Growth Industry Weight: This specifies the relative weighting of the growth industry variable in the analysis – we use the default value of 0.34
  • Income Weight: This specifies the relative weighting of the income variable in the analysis – we use the default value of 0.39
  • Government Benefits Weight: This specifies the relative weighting of the government benefits variable in the analysis – we use the default value of –0.42

Once you have entered all the parameters, click Add and Run to execute the tool

Outputs

Once your tool has run, click Display on the dialogue pop-up box that appears. This will open your new dataset (named Output: EmploymentProsperityIndex-Workflow XXX) in your data panel. It should look something like the image below

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Now we would like to compare our result to the EPI produced in the pre-packaged Economic Prosperity Index for Australia. To do this:

  • Select Employment Prosperity Index for Australia 2011 as your dataset, selecting all variables
  • Merge the two datasets together: Employment Prosperity Index for Australia 2011 and your generated dataset: Output: EmploymentProsperityIndex-Workflow XXX
  • Create and Interactive Scatterplot of the two EPI indices (x attribute: EPI 2011, y attribute: EPI)

Your scatterplot should look something like the image below

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This shows that our EPI that we generated ourselves is similar to the EPI produced for Australia by the Centre of Full Employment and Equity (CofFEE)