Benchmarking performance in regional development
•
1 like•717 views
Looking outside of your place to other places is a very sophisticated way of building policy. This presentation will use examples across regional NSW to demonstrate how you can use demographic and economic benchmarks to understand one place in the context of others and develop policy that plays to a place’s strengths.
Recommended
Recommended
More Related Content
Similar to Benchmarking performance in regional development
Similar to Benchmarking performance in regional development (20)
More from .id - the population experts
More from .id - the population experts (16)
Recently uploaded
Recently uploaded (20)
Benchmarking performance in regional development
- 1. 1 Benchmarking performance in regional development April 2013
- 2. 2 The Australian context 2006-2011
- 4. 4 Total births and fertility rates Australia,1971-2010 Source: ABS, Births Australia, 2010 0 0.5 1 1.5 2 2.5 3 3.5 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 AverageNo.ofchildren Births Year Births Fertilityrate
- 5. 5 Annual population change Australia 1971-2010 Source: ABS, Regional Population Growth, Australia, 2009-10 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% %change Period
- 6. 6 Total growth States, 2006-2011 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 NSW Vic Qld SA WA Tas NT ACT Absolute growth 2006-2011
- 7. 7 Comparative growth States, 2006-2011 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% NSW Vic Qld SA WA Tas NT ACT State % growth 2011 5 year growth %
- 8. 8 Significant demographic changes in Australia • Historic levels of population growth • Large increases in overseas migration gain • A reversal in fertility rate trends with decreases becoming minor increases (more babies) • Continued declining mortality rates (people living longer)
- 9. 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85+ Recent arrivals (2006-2011) Total population Source: 2011 Census of Population and Housing, ABS Age structure of migrants 2006-2011, compared to population
- 10. 10 Change in age structure Regional NSW, 2006-2011
- 11. 11 Benchmarking... • Through the demographic lens • Examples from regional NSW • National Economic Indicators Series • Other resources available
- 12. 12 Understand places through the demographic lens
- 13. 13 Building a narrative Data Context Narrative Role and function
- 14. 14 Role and function of places A) Residential role and function B) Economic role and function C) Relationship between A) and B)
- 15. 15 Age structure Regional NSW, 2011
- 16. 16 Tamworth
- 17. 17
- 18. 18
- 19. 19 Bathurst
- 20. 20 Orange
- 21. 21 Wagga Wagga
- 22. 22 Wagga Wagga
- 23. 23 Cessnock
- 24. 24 Maitland
- 25. 25 Dubbo
- 26. 26 Building a narrative Demographic data (raw materials) e.g. employment by industry Benchmarking (context) e.g. region, State, Australia, time Role and function (narrative) Identify strengths/ understand weaknesses Good policy
- 28. 31 Demographic resource centre National resources Local government resources http://id.com.au http://blog.id.com.au http://economic-indicators.id.com.au
Editor's Notes
- Looking outside of your place to other places is a very sophisticated way of building policy. This presentation will use examples across regional NSW to demonstrate how you can use demographic and economic benchmarks to understand one place in the context of others and develop policy that plays to a place’s strengths.
- This population growth is driven by an increasing birth rate, but more so by migration. Migration peaked in 2009 and has been much lower since, but the 2006-2011 period saw a very high number of people entering Australia.
- In addition, the fertility rate has gone back up to 2.0. So not only do we have more people of childbearing age, but they are being more productive! A perfect storm. The average number of children born in Australia each year since the mid 1980s has been fairly steady at around 250,000. This figure has now been rapidly rising since 2005 and reached 300,000 births in 2009. That’s an additional 120,000 births over and above the long term average since 2006. This is not a forecast or an estimate. This is actual births data. These children are in the system – no wonder preschools and primary schools are being overwhelmed.
- Australia has been through a period of record population growth since the 2006 Census
- Queensland historically the fastest growing State. Been replaced by WA as the fastest growing State. In 06-11 only 4th fastest behind WA, ACT and NT Talk of two-speed economy – the mining states – assumed Queensland is on the positive side of the ledger .... BUT Percentage of persons employed in mining is lower than WA – has less effect on GDP and consumption More trade exposed industries (tourism, manufacturing) Queensland does not do well with a high Oz dollar It’s all about relativities...which create push and pull factors
- There has been a major shift in population growth patterns in Australia
- The age structure of these migrants is one thing keeping the population of NSW young. It is highly concentrated between the ages of 15 and 35, more so than in any other state, probably due to the student population
- I will also launch the National Economic Indicators Series - .id’s national benchmarking resource that provides key economic indicators for every LGA in the country.
- I will also launch the National Economic Indicators Series - .id’s national benchmarking resource that provides key economic indicators for every LGA in the country.
- I will also launch the National Economic Indicators Series - .id’s national benchmarking resource that provides key economic indicators for every LGA in the country.
- With something like 200 councils using profile.id® over the past 15 years or so, a frequently asked question at council briefing and training sessions has been, “Can you extend profile.id® to include economic data?” These requests grew in number as Local Government’s role in economic development has grown. Quite simply, this was the inspiration to develop economy.id® – and now to extend it with an Impact Assessment model. However, getting reliable economic data for sub-State economies is a significant challenge and this blog discusses how we discovered a solution by partnering with economic brains trust, NIEIR. The challenges We started by evaluating what economic data was available in the Census. Having used Census Journey to Work data many (many) years ago for my Geography honours thesis, I was aware that Census Journey to Work destination data provided a good basis for profiling an economy at the local area level. The data provides in aggregate, a rough the number of jobs by industry sector for any local government area. However, while providing a reasonably accurate ‘shape’ of the economy (dominant and emerging industry sectors), it does not provide an adequate measure of economic activity at the local area level. For example, we know that Census counts of employment are notoriously inaccurate, with up to a 20% undercount due to the automatic exclusion of those persons who are employed but fail to state a location, or who can’t be coded to their exact workplace address. While Census data is an excellent resource for detailed worker characteristics (and is used in economy.id®), using it as the main source of broad employment numbers is problematic. Reliable primary economic data sets exist only at the national, state and regional level at best. Therefore the only way to get a realistic measure of jobs, output, turnover etc. at the local area level is to undertake economic modelling. The most significant challenge with local area economic modelling is to ensure that the process reflects the unique economic characteristics of the local area. For example, one of the traps is to apply national and state-level productivity propensities at the local level, which we now know is simply not accurate because it assumes a that the economic characteristics of all local areas are the same. The need for local nuance At .id we have been looking for a solution to this problem for quite some time. Enter Peter Brain from the National Institute for Economic and Industry Research (NIEIR). While Peter is famous for predicting major economic crises – namely the Asian Economic Crisis of 1997 and more recently the Global Financial Crisis – even more importantly, NIEIR’s reputation among the council officers familiar with their work preceded them and attracted .id’s interest in a partnership in developing economy.id®. NIEIR are recognised as industry leaders in the development and provision of robust economic modelling at the smallest credible geographic unit (Local Government Area). For over 10 years NIEIR have been producing the annual benchmark State of Regions Report commissioned by the Australian Local Government Association (ALGA). NIEIR modelling draws on many data sources to offer the most nuanced data possible at the local level. The NIEIR dataset is the result of a process of economic micro-simulation modelling – it is an amalgam of many different existing data sources (between 6 and 10 depending on the region and time period) which are synthesised to produce a series of estimates of the size and value of each industry. All the modelled data uses a breakdown of 19 ANZSIC industry divisions (e.g. Manufacturing) into 49 sub-groupings (e.g. Food Product Manufacturing) providing a highly detailed picture of which industries are contributing to the local economy. Importantly, the NIEIR model is updated on an annual basis (with quarterly breakdown for some characteristics). This means the impact of global, national and local economic changes can be clearly seen on each industry sector at the local level. We established that this modelling was superior to any other models we evaluated for the following reasons: Uses micro-simulation modelling (bringing together multiple data sources to simulate a realistic view of the local economy) Modelled annually ensuring the model is regularly updated to reflect global, national and local factors. Does not rely purely on Census counts of employment (Census data is a fantastic resource but is known to undercount employment by 20% because it excludes anyone who does not state their workplace address). Uses Centrelink, DEEWR labour market statistics, ATO data to provide a more accurate estimate of CURRENT employment. Estimates Hours Worked and converts this into a measure of Full-Time Equivalent Employment to ensure that underemployment cannot be hidden. Does not assume that the productivity of an industry sector is the same across all LGAs in the State, but uses ATO data to ensure local differences are taken into account. Uses locally derived inputs such as commercial building approvals by floor space, and Dun and Bradstreet datasets on business start-ups and exits to capture local industrial growth and decline. Makes manual adjustments are made to refine the model based on local knowledge such as arrival of new employers, closing industries, large building construction etc (clients can input into this process) Mining areas are treated differently to take into account fly-in-fly-out and other considerations. To benchmark we need a national dataset so we can see the relative position of every LGA. Problem - Economic data is not generally available for local areas such as sub-State regions or Local Government Areas. Economic modelers have the challenging task of taking national and state figures and modeling them down to these smaller geographic areas. Their task is to create an economic model that is the most accurate representation of economic reality that we can have. Criteria - I’m sure you will thank me if I don’t go into the intracacies of economic modelling here – but there are a couple of points I need to get across.In simple models, State wide data is taken and divided across LGAs as if they are all the same. For example the value of a mining job in Brisbane is treated in the same way as on in Mount Isa – even though they are patently not the same thing at all and can lead to all sorts of strange anomolies in the data. National Economics (NIEIR) are .id’s economic modelers of choice. We believe that their model is the most sophisticated and nuanced for local areas. Together we have built a set of economic indicators for every LGA in Australia as a starting point to understanding their areas’ contribution to the State and National economies. Today we present the National Economic Indicators Series for the first time. It will be publically available from .id’s website and updated annually. There are xx data series in the indicators, and I stress that this is only a starting point for analysis – but it does provide a very quick way of benchmarking one area to another and answering questions like? Which areas are similar Which areas are performing well Which areas area struggling economically So without further ado, here is the inaugural set of National Economic Indicators. What it won’t tell you is why … that’s where you need to build a more comprehensive story of your local economy –
- To benchmark successfully and build a narrative, you need access to data that is presented in a consistent way that enables you to draw comparisons both within and across regions.